{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/sashadasasha/2805/blob/main/Project_learn_task_.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F6EDfWQLkBLX",
        "tags": []
      },
      "source": [
        "## 1. Постановка задачи"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "G3NPCLzmkBLa"
      },
      "source": [
        "<center> <img src=https://storage.googleapis.com/kaggle-competitions/kaggle/3333/media/taxi_meter.png align=\"right\" width=\"300\"/> </center>\n",
        "    \n",
        "Вам предстоит решить настоящую задачу машинного обучения, направленную на автоматизацию бизнес процессов. Мы построим модель, которая будет предсказывать общую продолжительность поездки такси в Нью-Йорке.\n",
        "\n",
        "Представьте вы заказываете такси из одной точки Нью-Йорка в другую, причем не обязательно конечная точка должна находиться в пределах города. Сколько вы должны будете за нее заплатить? Известно, что стоимость такси в США  рассчитывается на основе фиксированной ставки + тарифная стоимость, величина которой зависит от времени и расстояния. Тарифы варьируются в зависимости от города.\n",
        "\n",
        "В свою очередь время поездки зависит от множества факторов таких как, откуда и куда вы едете, в какое время суток вы совершаете вашу поездку, погодных условий и так далее.\n",
        "\n",
        "Таким образом, если мы разработаем алгоритм, способный определять длительность поездки, мы сможем прогнозировать ее стоимость самым тривиальным образом, например, просто умножая стоимость на заданный тариф.\n",
        "Сервисы такси хранят огромные объёмы информации о поездках, включая такие данные как конечная, начальная точка маршрута, дата поездки и ее длительность. Эти данные можно использовать для того, чтобы прогнозировать длительность поездки в автоматическом режиме с привлечением искусственного интеллекта.\n",
        "\n",
        "**Бизнес-задача:** определить характеристики и с их помощью спрогнозировать длительность поездки такси.\n",
        "\n",
        "**Техническая задача для вас как для специалиста в Data Science:** построить модель машинного обучения, которая на основе предложенных характеристик клиента будет предсказывать числовой признак - время поездки такси. То есть решить задачу регрессии.\n",
        "\n",
        "**Основные цели проекта:**\n",
        "1. Сформировать набор данных на основе нескольких источников информации\n",
        "2. Спроектировать новые признаки с помощью Feature Engineering и выявить наиболее значимые при построении модели\n",
        "3. Исследовать предоставленные данные и выявить закономерности\n",
        "4. Построить несколько моделей и выбрать из них наилучшую по заданной метрике\n",
        "5. Спроектировать процесс предсказания времени длительности поездки для новых данных\n",
        "\n",
        "Загрузить свое решение на платформу Kaggle, тем самым поучаствовав в настоящем Data Science соревновании.\n",
        "Во время выполнения проекта вы отработаете навыки работы с несколькими источниками данных, генерации признаков, разведывательного анализа и визуализации данных, отбора признаков и, конечно же, построения моделей машинного обучения!\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4Up0IUABkBLc",
        "tags": []
      },
      "source": [
        "## 2. Знакомство с данными, базовый анализ и расширение данных"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7W9mZPhWkBLd"
      },
      "source": [
        "Начнём наше исследование со знакомства с предоставленными данными. А также подгрузим дополнительные источники данных и расширим наш исходный датасет.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UTIxJupXkBLd"
      },
      "source": [
        "Заранее импортируем модули, которые нам понадобятся для решения задачи:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 88,
      "metadata": {
        "id": "yeC12P0hkBLe"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import plotly.express as px\n",
        "\n",
        "from scipy import stats\n",
        "from sklearn import linear_model\n",
        "from sklearn import preprocessing\n",
        "from sklearn import model_selection\n",
        "from sklearn import tree\n",
        "from sklearn import ensemble\n",
        "from sklearn import metrics\n",
        "from sklearn import cluster\n",
        "from sklearn import feature_selection"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kk5-kjqikBLf"
      },
      "source": [
        "Прочитаем наш файл с исходными данными:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 89,
      "metadata": {
        "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
        "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a",
        "id": "hMQnRJwdkBLg",
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        },
        "outputId": "aaed1317-47fb-499a-fad6-4dae05350829"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/gdrive; to attempt to forcibly remount, call drive.mount(\"/content/gdrive\", force_remount=True).\n",
            "Train data shape: (1458644, 11)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
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              "          id  vendor_id      pickup_datetime     dropoff_datetime  \\\n",
              "0  id2875421          2  2016-03-14 17:24:55  2016-03-14 17:32:30   \n",
              "1  id2377394          1  2016-06-12 00:43:35  2016-06-12 00:54:38   \n",
              "2  id3858529          2  2016-01-19 11:35:24  2016-01-19 12:10:48   \n",
              "3  id3504673          2  2016-04-06 19:32:31  2016-04-06 19:39:40   \n",
              "4  id2181028          2  2016-03-26 13:30:55  2016-03-26 13:38:10   \n",
              "\n",
              "   passenger_count  pickup_longitude  pickup_latitude  dropoff_longitude  \\\n",
              "0                1        -73.982155        40.767937         -73.964630   \n",
              "1                1        -73.980415        40.738564         -73.999481   \n",
              "2                1        -73.979027        40.763939         -74.005333   \n",
              "3                1        -74.010040        40.719971         -74.012268   \n",
              "4                1        -73.973053        40.793209         -73.972923   \n",
              "\n",
              "   dropoff_latitude store_and_fwd_flag  trip_duration  \n",
              "0         40.765602                  N            455  \n",
              "1         40.731152                  N            663  \n",
              "2         40.710087                  N           2124  \n",
              "3         40.706718                  N            429  \n",
              "4         40.782520                  N            435  "
            ],
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              "      <td>2016-03-26 13:38:10</td>\n",
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              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
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              "        document.querySelector('#df-f2271f52-26f9-4ef3-b596-2d6c3cfaa430 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "taxi_data"
            }
          },
          "metadata": {},
          "execution_count": 89
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')\n",
        "\n",
        "taxi_data = pd.read_csv('gdrive/My Drive/train.csv')\n",
        "print('Train data shape: {}'.format(taxi_data.shape))\n",
        "taxi_data.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xhaxNt0XkBLh"
      },
      "source": [
        "Итак, у нас с вами есть данные о почти 1.5 миллионах поездок и 11 характеристиках, которые описывают каждую из поездок."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tXufvADjkBLi"
      },
      "source": [
        "Мы условно разделили признаки нескольких групп. Каждой из групп мы в дальнейшем уделим отдельное внимание.\n",
        "\n",
        "**Данные о клиенте и таксопарке:**\n",
        "* id - уникальный идентификатор поездки\n",
        "* vendor_id - уникальный идентификатор поставщика (таксопарка), связанного с записью поездки\n",
        "\n",
        "**Временные характеристики:**\n",
        "* pickup_datetime - дата и время, когда был включен счетчик поездки\n",
        "* dropoff_datetime - дата и время, когда счетчик был отключен\n",
        "\n",
        "**Географическая информация:**\n",
        "* pickup_longitude -  долгота, на которой был включен счетчик\n",
        "* pickup_latitude - широта, на которой был включен счетчик\n",
        "* dropoff_longitude - долгота, на которой счетчик был отключен\n",
        "* dropoff_latitude - широта, на которой счетчик был отключен\n",
        "\n",
        "**Прочие признаки:**\n",
        "* passenger_count - количество пассажиров в транспортном средстве (введенное водителем значение)\n",
        "* store_and_fwd_flag - флаг, который указывает, сохранилась ли запись о поездке в памяти транспортного средства перед отправкой поставщику. Y - хранить и пересылать, N - не хранить и не пересылать поездку.\n",
        "\n",
        "**Целевой признак:**\n",
        "* trip_duration - продолжительность поездки в секундах\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VvrcGynTkBLi"
      },
      "source": [
        "Для начала мы проведем базовый анализ того, насколько данные готовы к дальнейшей предобработке и анализу."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "33rsP7rTkBLj"
      },
      "source": [
        "### Задание 2.1\n",
        "Для начала посмотрим на временные рамки, в которых мы работаем с данными.\n",
        "\n",
        "Переведите признак pickup_datetime в тип данных datetime с форматом год-месяц-день час:минута:секунда (в функции pd.to_datetime() параметр format='%Y-%m-%d %H:%M:%S').\n",
        "\n",
        "Определите временные рамки (без учета времени), за которые представлены данные."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 90,
      "metadata": {
        "id": "cIVxwqW1kBLj",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6110380c-08c4-4ffb-9a12-9ff610268cc8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "max_time {} 2016-06-30 23:59:39\n",
            "min_time {} 2016-01-01 00:00:17\n"
          ]
        }
      ],
      "source": [
        "# ваш код здесь\n",
        "taxi_data['pickup_datetime'] = pd.to_datetime(taxi_data['pickup_datetime'], format='%Y-%m-%d %H:%M:%S')\n",
        "max_time = taxi_data['pickup_datetime'].max()\n",
        "min_time = taxi_data['pickup_datetime'].min()\n",
        "print(\"max_time {}\", max_time)\n",
        "print(\"min_time {}\", min_time)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "prw5RhKnkBLj"
      },
      "source": [
        "### Задание 2.2\n",
        "Посмотрим на пропуски.\n",
        "Сколько пропущенных значений присутствует в данных (суммарно по всем столбцам таблицы)?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 91,
      "metadata": {
        "id": "ug16JoZDkBLk",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f51b9471-a7f2-4653-e017-7273ddfabe0a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1458644 entries, 0 to 1458643\n",
            "Data columns (total 11 columns):\n",
            " #   Column              Non-Null Count    Dtype         \n",
            "---  ------              --------------    -----         \n",
            " 0   id                  1458644 non-null  object        \n",
            " 1   vendor_id           1458644 non-null  int64         \n",
            " 2   pickup_datetime     1458644 non-null  datetime64[ns]\n",
            " 3   dropoff_datetime    1458644 non-null  object        \n",
            " 4   passenger_count     1458644 non-null  int64         \n",
            " 5   pickup_longitude    1458644 non-null  float64       \n",
            " 6   pickup_latitude     1458644 non-null  float64       \n",
            " 7   dropoff_longitude   1458644 non-null  float64       \n",
            " 8   dropoff_latitude    1458644 non-null  float64       \n",
            " 9   store_and_fwd_flag  1458644 non-null  object        \n",
            " 10  trip_duration       1458644 non-null  int64         \n",
            "dtypes: datetime64[ns](1), float64(4), int64(3), object(3)\n",
            "memory usage: 122.4+ MB\n"
          ]
        }
      ],
      "source": [
        "# ваш код здесь\n",
        "taxi_data.info()\n",
        "\n",
        "#пропущенных значений нет"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZMMIyT1skBLk"
      },
      "source": [
        "### Задание 2.3\n",
        "Посмотрим на статистические характеристики некоторых признаков.\n",
        "\n",
        "а) Сколько уникальных таксопарков присутствует в данных?\n",
        "\n",
        "б) Каково максимальное количество пассажиров?\n",
        "\n",
        "в) Чему равна средняя и медианная длительность поездки? Ответ приведите в секундах и округлите до целого.\n",
        "\n",
        "г) Чему равно минимальное и максимальное время поездки (в секундах)?\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 92,
      "metadata": {
        "id": "UeJfdmIskBLk",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "outputId": "1cc5f72c-af49-44f0-e78b-892f8925f561"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          vendor_id                pickup_datetime  passenger_count  \\\n",
              "count  1.458644e+06                        1458644     1.458644e+06   \n",
              "mean   1.534950e+00  2016-04-01 10:10:24.940037120     1.664530e+00   \n",
              "min    1.000000e+00            2016-01-01 00:00:17     0.000000e+00   \n",
              "25%    1.000000e+00  2016-02-17 16:46:04.249999872     1.000000e+00   \n",
              "50%    2.000000e+00            2016-04-01 17:19:40     1.000000e+00   \n",
              "75%    2.000000e+00  2016-05-15 03:56:08.750000128     2.000000e+00   \n",
              "max    2.000000e+00            2016-06-30 23:59:39     9.000000e+00   \n",
              "std    4.987772e-01                            NaN     1.314242e+00   \n",
              "\n",
              "       pickup_longitude  pickup_latitude  dropoff_longitude  dropoff_latitude  \\\n",
              "count      1.458644e+06     1.458644e+06       1.458644e+06      1.458644e+06   \n",
              "mean      -7.397349e+01     4.075092e+01      -7.397342e+01      4.075180e+01   \n",
              "min       -1.219333e+02     3.435970e+01      -1.219333e+02      3.218114e+01   \n",
              "25%       -7.399187e+01     4.073735e+01      -7.399133e+01      4.073588e+01   \n",
              "50%       -7.398174e+01     4.075410e+01      -7.397975e+01      4.075452e+01   \n",
              "75%       -7.396733e+01     4.076836e+01      -7.396301e+01      4.076981e+01   \n",
              "max       -6.133553e+01     5.188108e+01      -6.133553e+01      4.392103e+01   \n",
              "std        7.090186e-02     3.288119e-02       7.064327e-02      3.589056e-02   \n",
              "\n",
              "       trip_duration  \n",
              "count   1.458644e+06  \n",
              "mean    9.594923e+02  \n",
              "min     1.000000e+00  \n",
              "25%     3.970000e+02  \n",
              "50%     6.620000e+02  \n",
              "75%     1.075000e+03  \n",
              "max     3.526282e+06  \n",
              "std     5.237432e+03  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-00ec07aa-f6b0-4b65-81b3-5adb31666299\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>vendor_id</th>\n",
              "      <th>pickup_datetime</th>\n",
              "      <th>passenger_count</th>\n",
              "      <th>pickup_longitude</th>\n",
              "      <th>pickup_latitude</th>\n",
              "      <th>dropoff_longitude</th>\n",
              "      <th>dropoff_latitude</th>\n",
              "      <th>trip_duration</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>1.458644e+06</td>\n",
              "      <td>1458644</td>\n",
              "      <td>1.458644e+06</td>\n",
              "      <td>1.458644e+06</td>\n",
              "      <td>1.458644e+06</td>\n",
              "      <td>1.458644e+06</td>\n",
              "      <td>1.458644e+06</td>\n",
              "      <td>1.458644e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>1.534950e+00</td>\n",
              "      <td>2016-04-01 10:10:24.940037120</td>\n",
              "      <td>1.664530e+00</td>\n",
              "      <td>-7.397349e+01</td>\n",
              "      <td>4.075092e+01</td>\n",
              "      <td>-7.397342e+01</td>\n",
              "      <td>4.075180e+01</td>\n",
              "      <td>9.594923e+02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>2016-01-01 00:00:17</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>-1.219333e+02</td>\n",
              "      <td>3.435970e+01</td>\n",
              "      <td>-1.219333e+02</td>\n",
              "      <td>3.218114e+01</td>\n",
              "      <td>1.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>2016-02-17 16:46:04.249999872</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>-7.399187e+01</td>\n",
              "      <td>4.073735e+01</td>\n",
              "      <td>-7.399133e+01</td>\n",
              "      <td>4.073588e+01</td>\n",
              "      <td>3.970000e+02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>2.000000e+00</td>\n",
              "      <td>2016-04-01 17:19:40</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>-7.398174e+01</td>\n",
              "      <td>4.075410e+01</td>\n",
              "      <td>-7.397975e+01</td>\n",
              "      <td>4.075452e+01</td>\n",
              "      <td>6.620000e+02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>2.000000e+00</td>\n",
              "      <td>2016-05-15 03:56:08.750000128</td>\n",
              "      <td>2.000000e+00</td>\n",
              "      <td>-7.396733e+01</td>\n",
              "      <td>4.076836e+01</td>\n",
              "      <td>-7.396301e+01</td>\n",
              "      <td>4.076981e+01</td>\n",
              "      <td>1.075000e+03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>2.000000e+00</td>\n",
              "      <td>2016-06-30 23:59:39</td>\n",
              "      <td>9.000000e+00</td>\n",
              "      <td>-6.133553e+01</td>\n",
              "      <td>5.188108e+01</td>\n",
              "      <td>-6.133553e+01</td>\n",
              "      <td>4.392103e+01</td>\n",
              "      <td>3.526282e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>4.987772e-01</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.314242e+00</td>\n",
              "      <td>7.090186e-02</td>\n",
              "      <td>3.288119e-02</td>\n",
              "      <td>7.064327e-02</td>\n",
              "      <td>3.589056e-02</td>\n",
              "      <td>5.237432e+03</td>\n",
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              "\n",
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              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-75271901-40a2-409c-b980-a66df53d2c61 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"#\\u0433) \\u043c\\u0438\\u043d\\u0438\\u043c\\u0430\\u043b\\u044c\\u043d\\u043e\\u0435 - 1\\u0431 \\u043c\\u0430\\u043a\\u0441\\u0438\\u043c\\u0430\\u043b\\u044c\\u043d\\u043e\\u0435 - 3526282\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"vendor_id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 515708.0250890787,\n        \"min\": 0.4987771539074,\n        \"max\": 1458644.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          1.5349502688798637,\n          0.4987771539074,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pickup_datetime\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"1970-01-01 00:00:00.001458644\",\n        \"max\": \"2016-06-30 23:59:39\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"1458644\",\n          \"2016-04-01 10:10:24.940037120\",\n          \"2016-05-15 03:56:08.750000128\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"passenger_count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 515707.7248263122,\n        \"min\": 0.0,\n        \"max\": 1458644.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          1458644.0,\n          1.6645295219395548,\n          9.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pickup_longitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 515732.73176924884,\n        \"min\": -121.93334197998048,\n        \"max\": 1458644.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          -73.97348630489282,\n          -73.96733093261719,\n          1458644.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pickup_latitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 515695.94133236835,\n        \"min\": 0.032881186257633054,\n        \"max\": 1458644.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          40.750920908391734,\n          40.768360137939446,\n          1458644.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"dropoff_longitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 515732.7314307756,\n        \"min\": -121.9333038330078,\n        \"max\": 1458644.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          -73.9734159469458,\n          -73.9630126953125,\n          1458644.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"dropoff_latitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 515696.4531646672,\n        \"min\": 0.03589055560563689,\n        \"max\": 1458644.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          40.7517995149002,\n          40.76980972290039,\n          1458644.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"trip_duration\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1278713.5878413154,\n        \"min\": 1.0,\n        \"max\": 3526282.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          959.4922729603659,\n          1075.0,\n          1458644.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 92
        }
      ],
      "source": [
        "# ваш код здесь\n",
        "taxi_data.describe()\n",
        "\n",
        "#a) 2 таксопарка\n",
        "#b) 9 пассажиров\n",
        "#в) средняя - 959, медианная (50 процентиль) - 662\n",
        "#г) минимальное - 1б максимальное - 3526282"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JhUgalQSkBLk"
      },
      "source": [
        "Займемся расширением исходного набора данных как с помощью внешних источников, так и с помощью манипуляций над имеющимися в данных признаками.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "riVyyTGRkBLk"
      },
      "source": [
        "### Задание 2.4\n",
        "Реализуйте функцию add_datetime_features(), которая принимает на вход таблицу с данными о поездках (DataFrame) и возвращает ту же таблицу с добавленными в нее 3 столбцами:\n",
        "* pickup_date - дата включения счетчика - начала поездки (без времени);\n",
        "* pickup_hour - час дня включения счетчика;\n",
        "* pickup_day_of_week - порядковый номер дня недели (число), в который был включен счетчик.\n",
        "\n",
        "а) Сколько поездок было совершено в субботу?\n",
        "\n",
        "б) Сколько поездок в среднем совершается в день? Ответ округлите до целого"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 93,
      "metadata": {
        "id": "2987aTUAkBLl",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a33e8207-d2d0-475e-886b-06b77c91f90b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "id                    8014.527473\n",
              "vendor_id             8014.527473\n",
              "pickup_datetime       8014.527473\n",
              "dropoff_datetime      8014.527473\n",
              "passenger_count       8014.527473\n",
              "pickup_longitude      8014.527473\n",
              "pickup_latitude       8014.527473\n",
              "dropoff_longitude     8014.527473\n",
              "dropoff_latitude      8014.527473\n",
              "store_and_fwd_flag    8014.527473\n",
              "trip_duration         8014.527473\n",
              "pickup_hour           8014.527473\n",
              "pickup_day_of_week    8014.527473\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 93
        }
      ],
      "source": [
        "def add_datetime_features(df):\n",
        "  df['pickup_date'] = df['pickup_datetime'].dt.date\n",
        "  df['pickup_hour'] = df['pickup_datetime'].dt.hour\n",
        "  df['pickup_day_of_week'] = df['pickup_datetime'].dt.dayofweek\n",
        "  return df\n",
        "\n",
        "taxi_data = add_datetime_features(taxi_data)\n",
        "#taxi_data.groupby(\"pickup_day_of_week\").count() #220868 v subbotu\n",
        "\n",
        "taxi_data.groupby('pickup_date').count().mean() #8015 поездок в среднем за день"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5rCk6lyTkBLl"
      },
      "source": [
        "### Задание 2.5\n",
        "Реализуйте функцию add_holiday_features(), которая принимает на вход две таблицы:\n",
        "* таблицу с данными о поездках;\n",
        "* таблицу с данными о праздничных днях;\n",
        "\n",
        "и возвращает обновленную таблицу с данными о поездках с добавленным в нее столбцом pickup_holiday - бинарным признаком того, начата ли поездка в праздничный день или нет (1 - да, 0 - нет).\n",
        "\n",
        "Чему равна медианная длительность поездки на такси в праздничные дни? Ответ приведите в секундах, округлив до целого.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 94,
      "metadata": {
        "id": "ZnPWsSMukBLl",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4488bb16-c9c5-4494-cac9-25c666a7b113"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "665.0"
            ]
          },
          "metadata": {},
          "execution_count": 94
        }
      ],
      "source": [
        "holiday_data = pd.read_csv('gdrive/My Drive/holiday_data.csv', sep=';')\n",
        "holiday_data['date'] = pd.to_datetime(holiday_data['date']).dt.date\n",
        "\n",
        "def add_holiday_features(taxi_data, holiday_data):\n",
        "  taxi_new = pd.merge(taxi_data, holiday_data, left_on='pickup_date', right_on='date', how=\"left\")\n",
        "  taxi_new['pickup_holiday'] = np.where(pd.isnull(taxi_new['holiday']), 1, 0)\n",
        "  return taxi_new\n",
        "\n",
        "taxi_new = add_holiday_features(taxi_data, holiday_data)\n",
        "taxi_new.loc[taxi_new['pickup_holiday'] == 1]['trip_duration'].median() #665 - ответ почему-то неверный, если верить SF\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YPxMlVMAkBLl"
      },
      "source": [
        "### Задание 2.6\n",
        "Реализуйте функцию add_osrm_features(), которая принимает на вход две таблицы:\n",
        "* таблицу с данными о поездках;\n",
        "* таблицу с данными из OSRM;\n",
        "\n",
        "и возвращает обновленную таблицу с данными о поездках с добавленными в нее 3 столбцами:\n",
        "* total_distance;\n",
        "* total_travel_time;\n",
        "* number_of_steps.\n",
        "\n",
        "а) Чему равна разница (в секундах) между медианной длительностью поездки в данных и медианной длительностью поездки, полученной из OSRM?\n",
        "\n",
        "В результате объединения таблиц у вас должны были получиться пропуски в столбцах с информацией из OSRM API. Это связано с тем, что для некоторых поездок не удалось выгрузить данные из веб источника.\n",
        "\n",
        "б) Сколько пропусков содержится в столбцах с информацией из OSRM API после объединения таблиц?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 95,
      "metadata": {
        "id": "uey_zFbwkBLm",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5cfbc0ef-be57-4045-d6ea-cc61b5b66afe"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "372.5"
            ]
          },
          "metadata": {},
          "execution_count": 95
        }
      ],
      "source": [
        "osrm_data = pd.read_csv('gdrive/My Drive/osrm_data_train.csv')\n",
        "\n",
        "def add_osrm_features(taxi_data, osrm_data):\n",
        "  return pd.merge(taxi_data, osrm_data[['id', 'total_distance', 'total_travel_time', 'number_of_steps']], left_on='id', right_on = 'id', how=\"left\")\n",
        "\n",
        "taxi_osrm = add_osrm_features(taxi_new, osrm_data)\n",
        "\n",
        "taxi_osrm['trip_duration'].median() - taxi_osrm['total_travel_time'].median()\n",
        "\n",
        "# ваш код здесь"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "taxi_osrm.info() #пропуски в одной строке (1458644 - 1458643)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XjSvPT-_q41i",
        "outputId": "ab8bfc80-f127-456a-d734-397723bd7262"
      },
      "execution_count": 96,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1458644 entries, 0 to 1458643\n",
            "Data columns (total 21 columns):\n",
            " #   Column              Non-Null Count    Dtype         \n",
            "---  ------              --------------    -----         \n",
            " 0   id                  1458644 non-null  object        \n",
            " 1   vendor_id           1458644 non-null  int64         \n",
            " 2   pickup_datetime     1458644 non-null  datetime64[ns]\n",
            " 3   dropoff_datetime    1458644 non-null  object        \n",
            " 4   passenger_count     1458644 non-null  int64         \n",
            " 5   pickup_longitude    1458644 non-null  float64       \n",
            " 6   pickup_latitude     1458644 non-null  float64       \n",
            " 7   dropoff_longitude   1458644 non-null  float64       \n",
            " 8   dropoff_latitude    1458644 non-null  float64       \n",
            " 9   store_and_fwd_flag  1458644 non-null  object        \n",
            " 10  trip_duration       1458644 non-null  int64         \n",
            " 11  pickup_date         1458644 non-null  object        \n",
            " 12  pickup_hour         1458644 non-null  int32         \n",
            " 13  pickup_day_of_week  1458644 non-null  int32         \n",
            " 14  day                 51122 non-null    object        \n",
            " 15  date                51122 non-null    object        \n",
            " 16  holiday             51122 non-null    object        \n",
            " 17  pickup_holiday      1458644 non-null  int64         \n",
            " 18  total_distance      1458643 non-null  float64       \n",
            " 19  total_travel_time   1458643 non-null  float64       \n",
            " 20  number_of_steps     1458643 non-null  float64       \n",
            "dtypes: datetime64[ns](1), float64(7), int32(2), int64(4), object(7)\n",
            "memory usage: 222.6+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lGQpi4erkBLm",
        "tags": []
      },
      "source": [
        "### Задание 2.7.\n",
        "Реализуйте функцию add_geographical_features(), которая принимает на вход таблицу с данными о поездках и возвращает обновленную таблицу с добавленными в нее 2 столбцами:\n",
        "* haversine_distance - расстояние Хаверсина между точкой, в которой был включен счетчик, и точкой, в которой счетчик был выключен;\n",
        "* direction - направление движения из точки, в которой был включен счетчик, в точку, в которой счетчик был выключен.\n",
        "\n",
        "Чему равно медианное расстояние Хаверсина поездок (в киллометрах)? Ответ округлите до сотых.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 97,
      "metadata": {
        "id": "ti1rljMGkBLm"
      },
      "outputs": [],
      "source": [
        "def get_haversine_distance(lat1, lng1, lat2, lng2):\n",
        "    # переводим углы в радианы\n",
        "    lat1, lng1, lat2, lng2 = map(np.radians, (lat1, lng1, lat2, lng2))\n",
        "    # радиус земли в километрах\n",
        "    EARTH_RADIUS = 6371\n",
        "    # считаем кратчайшее расстояние h по формуле Хаверсина\n",
        "    lat_delta = lat2 - lat1\n",
        "    lng_delta = lng2 - lng1\n",
        "    d = np.sin(lat_delta * 0.5) ** 2 + np.cos(lat1) * np.cos(lat2) * np.sin(lng_delta * 0.5) ** 2\n",
        "    h = 2 * EARTH_RADIUS * np.arcsin(np.sqrt(d))\n",
        "    return h\n",
        "\n",
        "def get_angle_direction(lat1, lng1, lat2, lng2):\n",
        "    # переводим углы в радианы\n",
        "    lat1, lng1, lat2, lng2 = map(np.radians, (lat1, lng1, lat2, lng2))\n",
        "    # считаем угол направления движения alpha по формуле угла пеленга\n",
        "    lng_delta_rad = lng2 - lng1\n",
        "    y = np.sin(lng_delta_rad) * np.cos(lat2)\n",
        "    x = np.cos(lat1) * np.sin(lat2) - np.sin(lat1) * np.cos(lat2) * np.cos(lng_delta_rad)\n",
        "    alpha = np.degrees(np.arctan2(y, x))\n",
        "    return alpha"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 98,
      "metadata": {
        "id": "zqIZyeHmkBLm",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f8eeeb7e-537d-46b5-d400-0d7c248e7bd6"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2.0937171329263693"
            ]
          },
          "metadata": {},
          "execution_count": 98
        }
      ],
      "source": [
        "# ваш код здесь\n",
        "def add_geographical_features(taxi_osrm):\n",
        "  taxi_osrm['haversine_distance'] = get_haversine_distance(taxi_osrm['pickup_latitude'], taxi_osrm['pickup_longitude'], taxi_osrm['dropoff_latitude'],taxi_osrm['dropoff_longitude'])\n",
        "  taxi_osrm['direction'] = get_angle_direction(taxi_osrm['pickup_latitude'], taxi_osrm['pickup_longitude'], taxi_osrm['dropoff_latitude'],taxi_osrm['dropoff_longitude'])\n",
        "  return taxi_osrm\n",
        "\n",
        "new_tax_osrm = add_geographical_features(taxi_osrm)\n",
        "\n",
        "new_tax_osrm['haversine_distance'].median() #2.09"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_WWydgB2kBLm"
      },
      "source": [
        "### Задание 2.8.\n",
        "Реализуйте функцию add_cluster_features(), которая принимает на вход таблицу с данными о поездках и обученный алгоритм кластеризации. Функция должна возвращать обновленную таблицу с добавленными в нее столбцом geo_cluster - географический кластер, к которому относится поездка.\n",
        "\n",
        "Сколько поездок содержится в наименьшем по размеру географическом кластере?\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 99,
      "metadata": {
        "id": "YxpWNYfakBLn",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 129
        },
        "outputId": "fafb00f3-e625-42e2-f245-c1f14ed9f43a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KMeans(n_clusters=10, random_state=42)"
            ],
            "text/html": [
              "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KMeans(n_clusters=10, random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KMeans</label><div class=\"sk-toggleable__content\"><pre>KMeans(n_clusters=10, random_state=42)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 99
        }
      ],
      "source": [
        "# создаем обучающую выборку из географических координат всех точек\n",
        "coords = np.hstack((taxi_data[['pickup_latitude', 'pickup_longitude']],\n",
        "                    taxi_data[['dropoff_latitude', 'dropoff_longitude']]))\n",
        "# обучаем алгоритм кластеризации\n",
        "kmeans = cluster.KMeans(n_clusters=10, random_state=42)\n",
        "kmeans.fit(coords)\n",
        "# ваш код здесь"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "\n",
        "def add_cluster_features(new_tax_osrm, kmeans):\n",
        "  coords = np.hstack((new_tax_osrm[['pickup_latitude', 'pickup_longitude']],\n",
        "                    new_tax_osrm[['dropoff_latitude', 'dropoff_longitude']]))\n",
        "  new_tax_osrm['geo_cluster'] = kmeans.predict(coords)\n",
        "  return new_tax_osrm\n",
        "\n",
        "tax_cluster = add_cluster_features(new_tax_osrm, kmeans)\n",
        "tax_cluster.groupby(\"geo_cluster\").count() #2 - наименьший размер кластера"
      ],
      "metadata": {
        "id": "NENYyjmIt1Vw",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 444
        },
        "outputId": "f0089adc-9328-4d70-aeb0-547dcd1df352"
      },
      "execution_count": 100,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                 id  vendor_id  pickup_datetime  dropoff_datetime  \\\n",
              "geo_cluster                                                         \n",
              "0            596467     596467           596467            596467   \n",
              "1              7233       7233             7233              7233   \n",
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              "9            383874     383874           383874            383874   \n",
              "\n",
              "             passenger_count  pickup_longitude  pickup_latitude  \\\n",
              "geo_cluster                                                       \n",
              "0                     596467            596467           596467   \n",
              "1                       7233              7233             7233   \n",
              "2                          2                 2                2   \n",
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              "8                          5                 5                5   \n",
              "9                     383874            383874           383874   \n",
              "\n",
              "             dropoff_longitude  dropoff_latitude  store_and_fwd_flag  ...  \\\n",
              "geo_cluster                                                           ...   \n",
              "0                       596467            596467              596467  ...   \n",
              "1                         7233              7233                7233  ...   \n",
              "2                            2                 2                   2  ...   \n",
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              "8                            5                 5                   5  ...   \n",
              "9                       383874            383874              383874  ...   \n",
              "\n",
              "             pickup_day_of_week    day   date  holiday  pickup_holiday  \\\n",
              "geo_cluster                                                              \n",
              "0                        596467  20345  20345    20345          596467   \n",
              "1                          7233    279    279      279            7233   \n",
              "2                             2      0      0        0               2   \n",
              "3                         27022   1128   1128     1128           27022   \n",
              "4                        359233  12795  12795    12795          359233   \n",
              "5                         43619   1831   1831     1831           43619   \n",
              "6                            18      0      0        0              18   \n",
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              "8                             5      0      0        0               5   \n",
              "9                        383874  12982  12982    12982          383874   \n",
              "\n",
              "             total_distance  total_travel_time  number_of_steps  \\\n",
              "geo_cluster                                                       \n",
              "0                    596467             596467           596467   \n",
              "1                      7232               7232             7232   \n",
              "2                         2                  2                2   \n",
              "3                     27022              27022            27022   \n",
              "4                    359233             359233           359233   \n",
              "5                     43619              43619            43619   \n",
              "6                        18                 18               18   \n",
              "7                     41171              41171            41171   \n",
              "8                         5                  5                5   \n",
              "9                    383874             383874           383874   \n",
              "\n",
              "             haversine_distance  direction  \n",
              "geo_cluster                                 \n",
              "0                        596467     596467  \n",
              "1                          7233       7233  \n",
              "2                             2          2  \n",
              "3                         27022      27022  \n",
              "4                        359233     359233  \n",
              "5                         43619      43619  \n",
              "6                            18         18  \n",
              "7                         41171      41171  \n",
              "8                             5          5  \n",
              "9                        383874     383874  \n",
              "\n",
              "[10 rows x 23 columns]"
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      "source": [
        "### Задание 2.9.\n",
        "Реализуйте функцию add_weather_features(), которая принимает на вход две таблицы:\n",
        "* таблицу с данными о поездках;\n",
        "* таблицу с данными о погодных условиях на каждый час;\n",
        "\n",
        "и возвращает обновленную таблицу с данными о поездках с добавленными в нее 5 столбцами:\n",
        "* temperature - температура;\n",
        "* visibility - видимость;\n",
        "* wind speed - средняя скорость ветра;\n",
        "* precip - количество осадков;\n",
        "* events - погодные явления.\n",
        "\n",
        "а) Сколько поездок было совершено в снежную погоду?\n",
        "\n",
        "В результате объединения у вас должны получиться записи, для которых в столбцах temperature, visibility, wind speed, precip, и events будут пропуски. Это связано с тем, что в таблице с данными о погодных условиях отсутствуют измерения для некоторых моментов времени, в которых включался счетчик поездки.\n",
        "\n",
        "б) Сколько процентов от общего количества наблюдений в таблице с данными о поездках занимают пропуски в столбцах с погодными условиями? Ответ приведите с точностью до сотых процента.\n"
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            "text/plain": [
              "                  time  temperature  windchill  heat index  humidity  \\\n",
              "0  2015-12-31 02:00:00          7.8        7.1         NaN      0.89   \n",
              "1  2015-12-31 03:00:00          7.2        5.9         NaN      0.90   \n",
              "2  2015-12-31 04:00:00          7.2        NaN         NaN      0.90   \n",
              "3  2015-12-31 05:00:00          7.2        5.9         NaN      0.86   \n",
              "4  2015-12-31 06:00:00          7.2        6.4         NaN      0.90   \n",
              "\n",
              "   pressure  dew Point  visibility  wind dir  wind speed  gust speed  precip  \\\n",
              "0    1017.0        6.1         8.0       NNE         5.6         0.0     0.8   \n",
              "1    1016.5        5.6        12.9  Variable         7.4         0.0     0.3   \n",
              "2    1016.7        5.6        12.9      Calm         0.0         0.0     0.0   \n",
              "3    1015.9        5.0        14.5        NW         7.4         0.0     0.0   \n",
              "4    1016.2        5.6        11.3      West         5.6         0.0     0.0   \n",
              "\n",
              "  events conditions        date  hour  \n",
              "0    NaN   Overcast  2015-12-31     2  \n",
              "1    NaN   Overcast  2015-12-31     3  \n",
              "2    NaN   Overcast  2015-12-31     4  \n",
              "3    NaN   Overcast  2015-12-31     5  \n",
              "4    NaN   Overcast  2015-12-31     6  "
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              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-0cf342f6-01dd-4c6d-b18b-491ba818d675 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-0cf342f6-01dd-4c6d-b18b-491ba818d675');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "      --bg-color: #E8F0FE;\n",
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              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
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              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "\n",
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              "  }\n",
              "\n",
              "  @keyframes spin {\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-767afd9f-f9bb-4b66-92f5-70ed556b9809 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "repr_error": "Out of range float values are not JSON compliant: nan"
            }
          },
          "metadata": {},
          "execution_count": 101
        }
      ],
      "source": [
        "weather_data = pd.read_csv('gdrive/My Drive/weather_data.csv')\n",
        "weather_data.head()\n",
        "\n",
        "# ваш код здесь"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def add_weather_features(taxi_data, weather_data):\n",
        "  weather_data['time'] = pd.to_datetime(\n",
        "          weather_data['time'],\n",
        "          format = '%Y-%m-%d %H:%M:%S'\n",
        "      )\n",
        "  weather_data['date'] = weather_data['time'].dt.date\n",
        "  weather_data['hour'] = weather_data['time'].dt.hour\n",
        "  weather_data = weather_data[[\n",
        "          'temperature',\n",
        "          'visibility',\n",
        "          'wind speed',\n",
        "          'precip',\n",
        "          'events',\n",
        "          'date',\n",
        "          'hour'\n",
        "      ]]\n",
        "  df_merged = taxi_data.merge(\n",
        "          weather_data,\n",
        "          how = 'left',\n",
        "          left_on = ['pickup_date', 'pickup_hour'],\n",
        "          right_on = ['date', 'hour']\n",
        "      )\n",
        "  df_new = df_merged.drop(['date_y', 'hour'], axis = 1)\n",
        "  return df_new\n",
        "\n",
        "taxi_data = add_weather_features(new_tax_osrm, weather_data)\n",
        "taxi_data"
      ],
      "metadata": {
        "id": "qNI4xXCBxbMi",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 635
        },
        "outputId": "2b58c756-fac3-4fb2-e8a0-f3906e746126"
      },
      "execution_count": 102,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                id  vendor_id     pickup_datetime     dropoff_datetime  \\\n",
              "0        id2875421          2 2016-03-14 17:24:55  2016-03-14 17:32:30   \n",
              "1        id2377394          1 2016-06-12 00:43:35  2016-06-12 00:54:38   \n",
              "2        id3858529          2 2016-01-19 11:35:24  2016-01-19 12:10:48   \n",
              "3        id3504673          2 2016-04-06 19:32:31  2016-04-06 19:39:40   \n",
              "4        id2181028          2 2016-03-26 13:30:55  2016-03-26 13:38:10   \n",
              "...            ...        ...                 ...                  ...   \n",
              "1458639  id2376096          2 2016-04-08 13:31:04  2016-04-08 13:44:02   \n",
              "1458640  id1049543          1 2016-01-10 07:35:15  2016-01-10 07:46:10   \n",
              "1458641  id2304944          2 2016-04-22 06:57:41  2016-04-22 07:10:25   \n",
              "1458642  id2714485          1 2016-01-05 15:56:26  2016-01-05 16:02:39   \n",
              "1458643  id1209952          1 2016-04-05 14:44:25  2016-04-05 14:47:43   \n",
              "\n",
              "         passenger_count  pickup_longitude  pickup_latitude  \\\n",
              "0                      1        -73.982155        40.767937   \n",
              "1                      1        -73.980415        40.738564   \n",
              "2                      1        -73.979027        40.763939   \n",
              "3                      1        -74.010040        40.719971   \n",
              "4                      1        -73.973053        40.793209   \n",
              "...                  ...               ...              ...   \n",
              "1458639                4        -73.982201        40.745522   \n",
              "1458640                1        -74.000946        40.747379   \n",
              "1458641                1        -73.959129        40.768799   \n",
              "1458642                1        -73.982079        40.749062   \n",
              "1458643                1        -73.979538        40.781750   \n",
              "\n",
              "         dropoff_longitude  dropoff_latitude store_and_fwd_flag  ...  \\\n",
              "0               -73.964630         40.765602                  N  ...   \n",
              "1               -73.999481         40.731152                  N  ...   \n",
              "2               -74.005333         40.710087                  N  ...   \n",
              "3               -74.012268         40.706718                  N  ...   \n",
              "4               -73.972923         40.782520                  N  ...   \n",
              "...                    ...               ...                ...  ...   \n",
              "1458639         -73.994911         40.740170                  N  ...   \n",
              "1458640         -73.970184         40.796547                  N  ...   \n",
              "1458641         -74.004433         40.707371                  N  ...   \n",
              "1458642         -73.974632         40.757107                  N  ...   \n",
              "1458643         -73.972809         40.790585                  N  ...   \n",
              "\n",
              "         total_travel_time number_of_steps  haversine_distance   direction  \\\n",
              "0                    164.9             5.0            1.498521   99.970196   \n",
              "1                    332.0             6.0            1.805507 -117.153768   \n",
              "2                    767.6            16.0            6.385098 -159.680165   \n",
              "3                    235.8             4.0            1.485498 -172.737700   \n",
              "4                    140.1             5.0            1.188588  179.473585   \n",
              "...                    ...             ...                 ...         ...   \n",
              "1458639              311.7             8.0            1.225080 -119.059338   \n",
              "1458640              589.6            11.0            6.049836   25.342196   \n",
              "1458641              642.9            10.0            7.824606 -150.788492   \n",
              "1458642              161.6             7.0            1.092564   35.033294   \n",
              "1458643               90.7             2.0            1.134042   29.969486   \n",
              "\n",
              "        geo_cluster temperature visibility  wind speed  precip  events  \n",
              "0                 9         4.4        8.0        27.8     0.3     NaN  \n",
              "1                 4        28.9       16.1         7.4     0.0     NaN  \n",
              "2                 4        -6.7       16.1        24.1     0.0     NaN  \n",
              "3                 4         7.2       16.1        25.9     0.0     NaN  \n",
              "4                 9         9.4       16.1         9.3     0.0     NaN  \n",
              "...             ...         ...        ...         ...     ...     ...  \n",
              "1458639           0         7.8       16.1        11.1     0.0     NaN  \n",
              "1458640           9         7.2        2.8        18.5     8.1    Rain  \n",
              "1458641           4        18.3       16.1         0.0     0.0     NaN  \n",
              "1458642           0        -2.8       16.1         9.3     0.0     NaN  \n",
              "1458643           9         2.2       16.1        16.7     0.0     NaN  \n",
              "\n",
              "[1458644 rows x 29 columns]"
            ],
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>vendor_id</th>\n",
              "      <th>pickup_datetime</th>\n",
              "      <th>dropoff_datetime</th>\n",
              "      <th>passenger_count</th>\n",
              "      <th>pickup_longitude</th>\n",
              "      <th>pickup_latitude</th>\n",
              "      <th>dropoff_longitude</th>\n",
              "      <th>dropoff_latitude</th>\n",
              "      <th>store_and_fwd_flag</th>\n",
              "      <th>...</th>\n",
              "      <th>total_travel_time</th>\n",
              "      <th>number_of_steps</th>\n",
              "      <th>haversine_distance</th>\n",
              "      <th>direction</th>\n",
              "      <th>geo_cluster</th>\n",
              "      <th>temperature</th>\n",
              "      <th>visibility</th>\n",
              "      <th>wind speed</th>\n",
              "      <th>precip</th>\n",
              "      <th>events</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>id2875421</td>\n",
              "      <td>2</td>\n",
              "      <td>2016-03-14 17:24:55</td>\n",
              "      <td>2016-03-14 17:32:30</td>\n",
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              "      <td>40.767937</td>\n",
              "      <td>-73.964630</td>\n",
              "      <td>40.765602</td>\n",
              "      <td>N</td>\n",
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              "      <td>164.9</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.498521</td>\n",
              "      <td>99.970196</td>\n",
              "      <td>9</td>\n",
              "      <td>4.4</td>\n",
              "      <td>8.0</td>\n",
              "      <td>27.8</td>\n",
              "      <td>0.3</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>id2377394</td>\n",
              "      <td>1</td>\n",
              "      <td>2016-06-12 00:43:35</td>\n",
              "      <td>2016-06-12 00:54:38</td>\n",
              "      <td>1</td>\n",
              "      <td>-73.980415</td>\n",
              "      <td>40.738564</td>\n",
              "      <td>-73.999481</td>\n",
              "      <td>40.731152</td>\n",
              "      <td>N</td>\n",
              "      <td>...</td>\n",
              "      <td>332.0</td>\n",
              "      <td>6.0</td>\n",
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              "      <td>-117.153768</td>\n",
              "      <td>4</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>id3858529</td>\n",
              "      <td>2</td>\n",
              "      <td>2016-01-19 11:35:24</td>\n",
              "      <td>2016-01-19 12:10:48</td>\n",
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              "      <td>-73.979027</td>\n",
              "      <td>40.763939</td>\n",
              "      <td>-74.005333</td>\n",
              "      <td>40.710087</td>\n",
              "      <td>N</td>\n",
              "      <td>...</td>\n",
              "      <td>767.6</td>\n",
              "      <td>16.0</td>\n",
              "      <td>6.385098</td>\n",
              "      <td>-159.680165</td>\n",
              "      <td>4</td>\n",
              "      <td>-6.7</td>\n",
              "      <td>16.1</td>\n",
              "      <td>24.1</td>\n",
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              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>id3504673</td>\n",
              "      <td>2</td>\n",
              "      <td>2016-04-06 19:32:31</td>\n",
              "      <td>2016-04-06 19:39:40</td>\n",
              "      <td>1</td>\n",
              "      <td>-74.010040</td>\n",
              "      <td>40.719971</td>\n",
              "      <td>-74.012268</td>\n",
              "      <td>40.706718</td>\n",
              "      <td>N</td>\n",
              "      <td>...</td>\n",
              "      <td>235.8</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1.485498</td>\n",
              "      <td>-172.737700</td>\n",
              "      <td>4</td>\n",
              "      <td>7.2</td>\n",
              "      <td>16.1</td>\n",
              "      <td>25.9</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>id2181028</td>\n",
              "      <td>2</td>\n",
              "      <td>2016-03-26 13:30:55</td>\n",
              "      <td>2016-03-26 13:38:10</td>\n",
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              "      <td>40.793209</td>\n",
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              "      <td>2016-04-08 13:31:04</td>\n",
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              "      <td>-73.982201</td>\n",
              "      <td>40.745522</td>\n",
              "      <td>-73.994911</td>\n",
              "      <td>40.740170</td>\n",
              "      <td>N</td>\n",
              "      <td>...</td>\n",
              "      <td>311.7</td>\n",
              "      <td>8.0</td>\n",
              "      <td>1.225080</td>\n",
              "      <td>-119.059338</td>\n",
              "      <td>0</td>\n",
              "      <td>7.8</td>\n",
              "      <td>16.1</td>\n",
              "      <td>11.1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1458640</th>\n",
              "      <td>id1049543</td>\n",
              "      <td>1</td>\n",
              "      <td>2016-01-10 07:35:15</td>\n",
              "      <td>2016-01-10 07:46:10</td>\n",
              "      <td>1</td>\n",
              "      <td>-74.000946</td>\n",
              "      <td>40.747379</td>\n",
              "      <td>-73.970184</td>\n",
              "      <td>40.796547</td>\n",
              "      <td>N</td>\n",
              "      <td>...</td>\n",
              "      <td>589.6</td>\n",
              "      <td>11.0</td>\n",
              "      <td>6.049836</td>\n",
              "      <td>25.342196</td>\n",
              "      <td>9</td>\n",
              "      <td>7.2</td>\n",
              "      <td>2.8</td>\n",
              "      <td>18.5</td>\n",
              "      <td>8.1</td>\n",
              "      <td>Rain</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1458641</th>\n",
              "      <td>id2304944</td>\n",
              "      <td>2</td>\n",
              "      <td>2016-04-22 06:57:41</td>\n",
              "      <td>2016-04-22 07:10:25</td>\n",
              "      <td>1</td>\n",
              "      <td>-73.959129</td>\n",
              "      <td>40.768799</td>\n",
              "      <td>-74.004433</td>\n",
              "      <td>40.707371</td>\n",
              "      <td>N</td>\n",
              "      <td>...</td>\n",
              "      <td>642.9</td>\n",
              "      <td>10.0</td>\n",
              "      <td>7.824606</td>\n",
              "      <td>-150.788492</td>\n",
              "      <td>4</td>\n",
              "      <td>18.3</td>\n",
              "      <td>16.1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1458642</th>\n",
              "      <td>id2714485</td>\n",
              "      <td>1</td>\n",
              "      <td>2016-01-05 15:56:26</td>\n",
              "      <td>2016-01-05 16:02:39</td>\n",
              "      <td>1</td>\n",
              "      <td>-73.982079</td>\n",
              "      <td>40.749062</td>\n",
              "      <td>-73.974632</td>\n",
              "      <td>40.757107</td>\n",
              "      <td>N</td>\n",
              "      <td>...</td>\n",
              "      <td>161.6</td>\n",
              "      <td>7.0</td>\n",
              "      <td>1.092564</td>\n",
              "      <td>35.033294</td>\n",
              "      <td>0</td>\n",
              "      <td>-2.8</td>\n",
              "      <td>16.1</td>\n",
              "      <td>9.3</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1458643</th>\n",
              "      <td>id1209952</td>\n",
              "      <td>1</td>\n",
              "      <td>2016-04-05 14:44:25</td>\n",
              "      <td>2016-04-05 14:47:43</td>\n",
              "      <td>1</td>\n",
              "      <td>-73.979538</td>\n",
              "      <td>40.781750</td>\n",
              "      <td>-73.972809</td>\n",
              "      <td>40.790585</td>\n",
              "      <td>N</td>\n",
              "      <td>...</td>\n",
              "      <td>90.7</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.134042</td>\n",
              "      <td>29.969486</td>\n",
              "      <td>9</td>\n",
              "      <td>2.2</td>\n",
              "      <td>16.1</td>\n",
              "      <td>16.7</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1458644 rows × 29 columns</p>\n",
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              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    (() => {\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "taxi_data"
            }
          },
          "metadata": {},
          "execution_count": 102
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "mask = taxi_data['events'] == 'Snow'\n",
        "taxi_data[mask].shape[0] #поездки в снег"
      ],
      "metadata": {
        "id": "VlFVX4_1-i23",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8600c990-4a06-41af-ca67-c1939a3cae48"
      },
      "execution_count": 103,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "13126"
            ]
          },
          "metadata": {},
          "execution_count": 103
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cols_null_perc = taxi_data.isnull().mean() * 100\n",
        "cols_with_null = cols_null_perc[\n",
        "    cols_null_perc > 0\n",
        "].sort_values(ascending = False)\n",
        "print('Пропуски в процентах:\\n', round(cols_with_null, 2))"
      ],
      "metadata": {
        "id": "24MKfjo5-r3P",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bff30157-ad3e-4599-99d6-d8322074f194"
      },
      "execution_count": 104,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Пропуски в процентах:\n",
            " day                  96.50\n",
            "date_x               96.50\n",
            "holiday              96.50\n",
            "events               95.10\n",
            "temperature           0.82\n",
            "visibility            0.82\n",
            "wind speed            0.82\n",
            "precip                0.82\n",
            "number_of_steps       0.00\n",
            "total_distance        0.00\n",
            "total_travel_time     0.00\n",
            "dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hwsluTQHkBLn"
      },
      "source": [
        "### Задание 2.10.\n",
        "Реализуйте функцию fill_null_weather_data(), которая принимает на вход которая принимает на вход таблицу с данными о поездках. Функция должна заполнять пропущенные значения в столбцах.\n",
        "\n",
        "Пропуски в столбцах с погодными условиями -  temperature, visibility, wind speed, precip заполните медианным значением температуры, влажности, скорости ветра и видимости в зависимости от даты начала поездки. Для этого сгруппируйте данные по столбцу pickup_date и рассчитайте медиану в каждой группе, после чего с помощью комбинации методов transform() и fillna() заполните пропуски.\n",
        "Пропуски в столбце events заполните строкой 'None' - символом отсутствия погодных явлений (снега/дождя/тумана).\n",
        "\n",
        "Пропуски в столбцах с информацией из OSRM API - total_distance, total_travel_time и number_of_steps заполните медианным значением по столбцам.\n",
        "\n",
        "Чему равна медиана в столбце temperature после заполнения пропусков? Ответ округлите до десятых.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 105,
      "metadata": {
        "id": "oHIYqxjKkBLn",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 635
        },
        "outputId": "ae029f83-f079-463a-92ea-aca01d3242d3"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                id  vendor_id     pickup_datetime     dropoff_datetime  \\\n",
              "0        id2875421          2 2016-03-14 17:24:55  2016-03-14 17:32:30   \n",
              "1        id2377394          1 2016-06-12 00:43:35  2016-06-12 00:54:38   \n",
              "2        id3858529          2 2016-01-19 11:35:24  2016-01-19 12:10:48   \n",
              "3        id3504673          2 2016-04-06 19:32:31  2016-04-06 19:39:40   \n",
              "4        id2181028          2 2016-03-26 13:30:55  2016-03-26 13:38:10   \n",
              "...            ...        ...                 ...                  ...   \n",
              "1458639  id2376096          2 2016-04-08 13:31:04  2016-04-08 13:44:02   \n",
              "1458640  id1049543          1 2016-01-10 07:35:15  2016-01-10 07:46:10   \n",
              "1458641  id2304944          2 2016-04-22 06:57:41  2016-04-22 07:10:25   \n",
              "1458642  id2714485          1 2016-01-05 15:56:26  2016-01-05 16:02:39   \n",
              "1458643  id1209952          1 2016-04-05 14:44:25  2016-04-05 14:47:43   \n",
              "\n",
              "         passenger_count  pickup_longitude  pickup_latitude  \\\n",
              "0                      1        -73.982155        40.767937   \n",
              "1                      1        -73.980415        40.738564   \n",
              "2                      1        -73.979027        40.763939   \n",
              "3                      1        -74.010040        40.719971   \n",
              "4                      1        -73.973053        40.793209   \n",
              "...                  ...               ...              ...   \n",
              "1458639                4        -73.982201        40.745522   \n",
              "1458640                1        -74.000946        40.747379   \n",
              "1458641                1        -73.959129        40.768799   \n",
              "1458642                1        -73.982079        40.749062   \n",
              "1458643                1        -73.979538        40.781750   \n",
              "\n",
              "         dropoff_longitude  dropoff_latitude store_and_fwd_flag  ...  \\\n",
              "0               -73.964630         40.765602                  N  ...   \n",
              "1               -73.999481         40.731152                  N  ...   \n",
              "2               -74.005333         40.710087                  N  ...   \n",
              "3               -74.012268         40.706718                  N  ...   \n",
              "4               -73.972923         40.782520                  N  ...   \n",
              "...                    ...               ...                ...  ...   \n",
              "1458639         -73.994911         40.740170                  N  ...   \n",
              "1458640         -73.970184         40.796547                  N  ...   \n",
              "1458641         -74.004433         40.707371                  N  ...   \n",
              "1458642         -73.974632         40.757107                  N  ...   \n",
              "1458643         -73.972809         40.790585                  N  ...   \n",
              "\n",
              "         total_travel_time number_of_steps  haversine_distance   direction  \\\n",
              "0                    164.9             5.0            1.498521   99.970196   \n",
              "1                    332.0             6.0            1.805507 -117.153768   \n",
              "2                    767.6            16.0            6.385098 -159.680165   \n",
              "3                    235.8             4.0            1.485498 -172.737700   \n",
              "4                    140.1             5.0            1.188588  179.473585   \n",
              "...                    ...             ...                 ...         ...   \n",
              "1458639              311.7             8.0            1.225080 -119.059338   \n",
              "1458640              589.6            11.0            6.049836   25.342196   \n",
              "1458641              642.9            10.0            7.824606 -150.788492   \n",
              "1458642              161.6             7.0            1.092564   35.033294   \n",
              "1458643               90.7             2.0            1.134042   29.969486   \n",
              "\n",
              "        geo_cluster temperature visibility  wind speed  precip  events  \n",
              "0                 9         4.4        8.0        27.8     0.3    None  \n",
              "1                 4        28.9       16.1         7.4     0.0    None  \n",
              "2                 4        -6.7       16.1        24.1     0.0    None  \n",
              "3                 4         7.2       16.1        25.9     0.0    None  \n",
              "4                 9         9.4       16.1         9.3     0.0    None  \n",
              "...             ...         ...        ...         ...     ...     ...  \n",
              "1458639           0         7.8       16.1        11.1     0.0    None  \n",
              "1458640           9         7.2        2.8        18.5     8.1    Rain  \n",
              "1458641           4        18.3       16.1         0.0     0.0    None  \n",
              "1458642           0        -2.8       16.1         9.3     0.0    None  \n",
              "1458643           9         2.2       16.1        16.7     0.0    None  \n",
              "\n",
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              "      <th>vendor_id</th>\n",
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              "      <th>dropoff_latitude</th>\n",
              "      <th>store_and_fwd_flag</th>\n",
              "      <th>...</th>\n",
              "      <th>total_travel_time</th>\n",
              "      <th>number_of_steps</th>\n",
              "      <th>haversine_distance</th>\n",
              "      <th>direction</th>\n",
              "      <th>geo_cluster</th>\n",
              "      <th>temperature</th>\n",
              "      <th>visibility</th>\n",
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              "      <th>events</th>\n",
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              "      <th>0</th>\n",
              "      <td>id2875421</td>\n",
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              "      <td>2016-03-14 17:24:55</td>\n",
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              "      <td>99.970196</td>\n",
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              "      <td>27.8</td>\n",
              "      <td>0.3</td>\n",
              "      <td>None</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>id2377394</td>\n",
              "      <td>1</td>\n",
              "      <td>2016-06-12 00:43:35</td>\n",
              "      <td>2016-06-12 00:54:38</td>\n",
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              "      <td>40.738564</td>\n",
              "      <td>-73.999481</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>id3858529</td>\n",
              "      <td>2</td>\n",
              "      <td>2016-01-19 11:35:24</td>\n",
              "      <td>2016-01-19 12:10:48</td>\n",
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              "      <td>-73.979027</td>\n",
              "      <td>40.763939</td>\n",
              "      <td>-74.005333</td>\n",
              "      <td>40.710087</td>\n",
              "      <td>N</td>\n",
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              "      <td>767.6</td>\n",
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              "      <td>-159.680165</td>\n",
              "      <td>4</td>\n",
              "      <td>-6.7</td>\n",
              "      <td>16.1</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>id3504673</td>\n",
              "      <td>2</td>\n",
              "      <td>2016-04-06 19:32:31</td>\n",
              "      <td>2016-04-06 19:39:40</td>\n",
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              "      <td>40.719971</td>\n",
              "      <td>-74.012268</td>\n",
              "      <td>40.706718</td>\n",
              "      <td>N</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>id2181028</td>\n",
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              "      <td>2016-03-26 13:30:55</td>\n",
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              "      <td>2016-04-08 13:31:04</td>\n",
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              "      <td>-119.059338</td>\n",
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              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>1458640</th>\n",
              "      <td>id1049543</td>\n",
              "      <td>1</td>\n",
              "      <td>2016-01-10 07:35:15</td>\n",
              "      <td>2016-01-10 07:46:10</td>\n",
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              "      <td>40.747379</td>\n",
              "      <td>-73.970184</td>\n",
              "      <td>40.796547</td>\n",
              "      <td>N</td>\n",
              "      <td>...</td>\n",
              "      <td>589.6</td>\n",
              "      <td>11.0</td>\n",
              "      <td>6.049836</td>\n",
              "      <td>25.342196</td>\n",
              "      <td>9</td>\n",
              "      <td>7.2</td>\n",
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              "      <td>18.5</td>\n",
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              "    <tr>\n",
              "      <th>1458641</th>\n",
              "      <td>id2304944</td>\n",
              "      <td>2</td>\n",
              "      <td>2016-04-22 06:57:41</td>\n",
              "      <td>2016-04-22 07:10:25</td>\n",
              "      <td>1</td>\n",
              "      <td>-73.959129</td>\n",
              "      <td>40.768799</td>\n",
              "      <td>-74.004433</td>\n",
              "      <td>40.707371</td>\n",
              "      <td>N</td>\n",
              "      <td>...</td>\n",
              "      <td>642.9</td>\n",
              "      <td>10.0</td>\n",
              "      <td>7.824606</td>\n",
              "      <td>-150.788492</td>\n",
              "      <td>4</td>\n",
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              "      <td>16.1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>1458642</th>\n",
              "      <td>id2714485</td>\n",
              "      <td>1</td>\n",
              "      <td>2016-01-05 15:56:26</td>\n",
              "      <td>2016-01-05 16:02:39</td>\n",
              "      <td>1</td>\n",
              "      <td>-73.982079</td>\n",
              "      <td>40.749062</td>\n",
              "      <td>-73.974632</td>\n",
              "      <td>40.757107</td>\n",
              "      <td>N</td>\n",
              "      <td>...</td>\n",
              "      <td>161.6</td>\n",
              "      <td>7.0</td>\n",
              "      <td>1.092564</td>\n",
              "      <td>35.033294</td>\n",
              "      <td>0</td>\n",
              "      <td>-2.8</td>\n",
              "      <td>16.1</td>\n",
              "      <td>9.3</td>\n",
              "      <td>0.0</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1458643</th>\n",
              "      <td>id1209952</td>\n",
              "      <td>1</td>\n",
              "      <td>2016-04-05 14:44:25</td>\n",
              "      <td>2016-04-05 14:47:43</td>\n",
              "      <td>1</td>\n",
              "      <td>-73.979538</td>\n",
              "      <td>40.781750</td>\n",
              "      <td>-73.972809</td>\n",
              "      <td>40.790585</td>\n",
              "      <td>N</td>\n",
              "      <td>...</td>\n",
              "      <td>90.7</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.134042</td>\n",
              "      <td>29.969486</td>\n",
              "      <td>9</td>\n",
              "      <td>2.2</td>\n",
              "      <td>16.1</td>\n",
              "      <td>16.7</td>\n",
              "      <td>0.0</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
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              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-ac215f0a-68f3-49f5-a1f6-1b970057a158 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "taxi_with_fill_null"
            }
          },
          "metadata": {},
          "execution_count": 105
        }
      ],
      "source": [
        "# ваш код здесь\n",
        "def fill_null_weather_data(taxi_data):\n",
        "  weather_cols = ['temperature', 'visibility', 'wind speed', 'precip']\n",
        "  for col in weather_cols:\n",
        "      taxi_data[col] = taxi_data[col].fillna(\n",
        "          taxi_data.groupby('pickup_date')[col].transform('median')\n",
        "      )\n",
        "\n",
        "  taxi_data['events'] = taxi_data['events'].fillna('None')\n",
        "\n",
        "  values = {\n",
        "          'total_distance': taxi_data['total_distance'].median(),\n",
        "          'total_travel_time': taxi_data['total_travel_time'].median(),\n",
        "          'number_of_steps': taxi_data['number_of_steps'].median()\n",
        "    }\n",
        "  taxi_data = taxi_data.fillna(values)\n",
        "  return taxi_data\n",
        "\n",
        "taxi_with_fill_null = fill_null_weather_data(taxi_data)\n",
        "\n",
        "taxi_with_fill_null"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "round(taxi_with_fill_null['temperature'].median(), 1) #значение медианы температуры"
      ],
      "metadata": {
        "id": "2MD8-c3aSOZe",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3424da42-1668-451c-b9be-c5ee64ab13bf"
      },
      "execution_count": 106,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "11.1"
            ]
          },
          "metadata": {},
          "execution_count": 106
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MahzwVDxkBLn"
      },
      "source": [
        "В завершение первой части найдем очевидные выбросы в целевой переменной - длительности поездки.\n",
        "\n",
        "Проще всего найти слишком продолжительные поездки. Давайте условимся, что выбросами будут считаться поездки, длительность которых превышает 24 часа.\n",
        "\n",
        "Чуть сложнее с анализом поездок, длительность которых слишком мала. Потому что к ним относятся действительно реальные поездки на короткие расстояния, поездки, которые были отменены через секунду после того как включился счетчик, а также “телепортации” - перемещение на большие расстояния за считанные секунды.\n",
        "Условимся, что мы будем считать выбросами только последнюю группу. Как же нам их обнаружить наиболее простым способом?\n",
        "\n",
        "Можно воспользоваться информацией о кратчайшем расстоянии, которое проезжает такси. Вычислить среднюю скорость автомобиля на кратчайшем пути следующим образом:\n",
        "$$avg\\_speed= \\frac{total\\_distance}{1000*trip\\_duration}*3600$$\n",
        "Если мы построим диаграмму рассеяния средней скорости движения автомобилей, мы увидим следующую картину:\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 107,
      "metadata": {
        "id": "Nnp1KatIkBLo",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 465
        },
        "outputId": "5f69a290-e568-47cb-fa61-62976a3b16db"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "avg_speed = taxi_with_fill_null['total_distance'] / taxi_with_fill_null['trip_duration'] * 3.6\n",
        "fig, ax = plt.subplots(figsize=(10, 5))\n",
        "sns.scatterplot(x=avg_speed.index, y=avg_speed, ax=ax)\n",
        "ax.set_xlabel('Index')\n",
        "ax.set_ylabel('Average speed');"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BGxXxv7LkBLo"
      },
      "source": [
        "Как раз отсюда мы видим, что у нас есть “поездки-телепортации”, для которых средняя скорость более 1000 км/ч. Даже есть такая, средняя скорость которой составляла более 12000 км/ч!\n",
        "\n",
        "Давайте условимся, что предельная средняя скорость, которую могут развивать таксисты будет 300 км/ч.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sSWHdHNWkBLo",
        "tags": []
      },
      "source": [
        "### Задание 2.11.\n",
        "Найдите поездки, длительность которых превышает 24 часа. И удалите их из набора данных.\n",
        "\n",
        "а) Сколько выбросов по признаку длительности поездки вам удалось найти?\n",
        "\n",
        "Найдите поездки, средняя скорость которых по кратчайшему пути превышает 300 км/ч и удалите их из данных.\n",
        "\n",
        "б) Сколько выбросов по признаку скорости вам удалось найти?"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "taxi_with_fill_null[taxi_with_fill_null['trip_duration'] > 86400].shape[0] #выбросы по длительности"
      ],
      "metadata": {
        "id": "2pvHcT9ySu8O",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5fc1cb96-0ebc-4ec8-d916-7749f6074e98"
      },
      "execution_count": 108,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4"
            ]
          },
          "metadata": {},
          "execution_count": 108
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 109,
      "metadata": {
        "id": "3KVmlq8SkBLo",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "dd1b78c6-93a3-4f30-bc99-d7bfef3f366b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "407"
            ]
          },
          "metadata": {},
          "execution_count": 109
        }
      ],
      "source": [
        "avg_speed[avg_speed > 300].shape[0] #выбросы по скорости"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Убираем выбросы"
      ],
      "metadata": {
        "id": "tbvt0Ga-TCC-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "norm_duration = taxi_with_fill_null['trip_duration'] <= 86400\n",
        "norm_distance = (taxi_with_fill_null['total_distance'] / taxi_with_fill_null['trip_duration'] * 3.6) <= 300\n",
        "taxi_data = taxi_with_fill_null[norm_duration & norm_distance]\n",
        "taxi_data"
      ],
      "metadata": {
        "id": "STla0nnLTGCL",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 635
        },
        "outputId": "316f1d48-752f-4544-bfae-10b865e81744"
      },
      "execution_count": 110,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                id  vendor_id     pickup_datetime     dropoff_datetime  \\\n",
              "0        id2875421          2 2016-03-14 17:24:55  2016-03-14 17:32:30   \n",
              "1        id2377394          1 2016-06-12 00:43:35  2016-06-12 00:54:38   \n",
              "2        id3858529          2 2016-01-19 11:35:24  2016-01-19 12:10:48   \n",
              "3        id3504673          2 2016-04-06 19:32:31  2016-04-06 19:39:40   \n",
              "4        id2181028          2 2016-03-26 13:30:55  2016-03-26 13:38:10   \n",
              "...            ...        ...                 ...                  ...   \n",
              "1458639  id2376096          2 2016-04-08 13:31:04  2016-04-08 13:44:02   \n",
              "1458640  id1049543          1 2016-01-10 07:35:15  2016-01-10 07:46:10   \n",
              "1458641  id2304944          2 2016-04-22 06:57:41  2016-04-22 07:10:25   \n",
              "1458642  id2714485          1 2016-01-05 15:56:26  2016-01-05 16:02:39   \n",
              "1458643  id1209952          1 2016-04-05 14:44:25  2016-04-05 14:47:43   \n",
              "\n",
              "         passenger_count  pickup_longitude  pickup_latitude  \\\n",
              "0                      1        -73.982155        40.767937   \n",
              "1                      1        -73.980415        40.738564   \n",
              "2                      1        -73.979027        40.763939   \n",
              "3                      1        -74.010040        40.719971   \n",
              "4                      1        -73.973053        40.793209   \n",
              "...                  ...               ...              ...   \n",
              "1458639                4        -73.982201        40.745522   \n",
              "1458640                1        -74.000946        40.747379   \n",
              "1458641                1        -73.959129        40.768799   \n",
              "1458642                1        -73.982079        40.749062   \n",
              "1458643                1        -73.979538        40.781750   \n",
              "\n",
              "         dropoff_longitude  dropoff_latitude store_and_fwd_flag  ...  \\\n",
              "0               -73.964630         40.765602                  N  ...   \n",
              "1               -73.999481         40.731152                  N  ...   \n",
              "2               -74.005333         40.710087                  N  ...   \n",
              "3               -74.012268         40.706718                  N  ...   \n",
              "4               -73.972923         40.782520                  N  ...   \n",
              "...                    ...               ...                ...  ...   \n",
              "1458639         -73.994911         40.740170                  N  ...   \n",
              "1458640         -73.970184         40.796547                  N  ...   \n",
              "1458641         -74.004433         40.707371                  N  ...   \n",
              "1458642         -73.974632         40.757107                  N  ...   \n",
              "1458643         -73.972809         40.790585                  N  ...   \n",
              "\n",
              "         total_travel_time number_of_steps  haversine_distance   direction  \\\n",
              "0                    164.9             5.0            1.498521   99.970196   \n",
              "1                    332.0             6.0            1.805507 -117.153768   \n",
              "2                    767.6            16.0            6.385098 -159.680165   \n",
              "3                    235.8             4.0            1.485498 -172.737700   \n",
              "4                    140.1             5.0            1.188588  179.473585   \n",
              "...                    ...             ...                 ...         ...   \n",
              "1458639              311.7             8.0            1.225080 -119.059338   \n",
              "1458640              589.6            11.0            6.049836   25.342196   \n",
              "1458641              642.9            10.0            7.824606 -150.788492   \n",
              "1458642              161.6             7.0            1.092564   35.033294   \n",
              "1458643               90.7             2.0            1.134042   29.969486   \n",
              "\n",
              "        geo_cluster temperature visibility  wind speed  precip  events  \n",
              "0                 9         4.4        8.0        27.8     0.3    None  \n",
              "1                 4        28.9       16.1         7.4     0.0    None  \n",
              "2                 4        -6.7       16.1        24.1     0.0    None  \n",
              "3                 4         7.2       16.1        25.9     0.0    None  \n",
              "4                 9         9.4       16.1         9.3     0.0    None  \n",
              "...             ...         ...        ...         ...     ...     ...  \n",
              "1458639           0         7.8       16.1        11.1     0.0    None  \n",
              "1458640           9         7.2        2.8        18.5     8.1    Rain  \n",
              "1458641           4        18.3       16.1         0.0     0.0    None  \n",
              "1458642           0        -2.8       16.1         9.3     0.0    None  \n",
              "1458643           9         2.2       16.1        16.7     0.0    None  \n",
              "\n",
              "[1458233 rows x 29 columns]"
            ],
            "text/html": [
              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>vendor_id</th>\n",
              "      <th>pickup_datetime</th>\n",
              "      <th>dropoff_datetime</th>\n",
              "      <th>passenger_count</th>\n",
              "      <th>pickup_longitude</th>\n",
              "      <th>pickup_latitude</th>\n",
              "      <th>dropoff_longitude</th>\n",
              "      <th>dropoff_latitude</th>\n",
              "      <th>store_and_fwd_flag</th>\n",
              "      <th>...</th>\n",
              "      <th>total_travel_time</th>\n",
              "      <th>number_of_steps</th>\n",
              "      <th>haversine_distance</th>\n",
              "      <th>direction</th>\n",
              "      <th>geo_cluster</th>\n",
              "      <th>temperature</th>\n",
              "      <th>visibility</th>\n",
              "      <th>wind speed</th>\n",
              "      <th>precip</th>\n",
              "      <th>events</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>id2875421</td>\n",
              "      <td>2</td>\n",
              "      <td>2016-03-14 17:24:55</td>\n",
              "      <td>2016-03-14 17:32:30</td>\n",
              "      <td>1</td>\n",
              "      <td>-73.982155</td>\n",
              "      <td>40.767937</td>\n",
              "      <td>-73.964630</td>\n",
              "      <td>40.765602</td>\n",
              "      <td>N</td>\n",
              "      <td>...</td>\n",
              "      <td>164.9</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.498521</td>\n",
              "      <td>99.970196</td>\n",
              "      <td>9</td>\n",
              "      <td>4.4</td>\n",
              "      <td>8.0</td>\n",
              "      <td>27.8</td>\n",
              "      <td>0.3</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>id2377394</td>\n",
              "      <td>1</td>\n",
              "      <td>2016-06-12 00:43:35</td>\n",
              "      <td>2016-06-12 00:54:38</td>\n",
              "      <td>1</td>\n",
              "      <td>-73.980415</td>\n",
              "      <td>40.738564</td>\n",
              "      <td>-73.999481</td>\n",
              "      <td>40.731152</td>\n",
              "      <td>N</td>\n",
              "      <td>...</td>\n",
              "      <td>332.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>1.805507</td>\n",
              "      <td>-117.153768</td>\n",
              "      <td>4</td>\n",
              "      <td>28.9</td>\n",
              "      <td>16.1</td>\n",
              "      <td>7.4</td>\n",
              "      <td>0.0</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>id3858529</td>\n",
              "      <td>2</td>\n",
              "      <td>2016-01-19 11:35:24</td>\n",
              "      <td>2016-01-19 12:10:48</td>\n",
              "      <td>1</td>\n",
              "      <td>-73.979027</td>\n",
              "      <td>40.763939</td>\n",
              "      <td>-74.005333</td>\n",
              "      <td>40.710087</td>\n",
              "      <td>N</td>\n",
              "      <td>...</td>\n",
              "      <td>767.6</td>\n",
              "      <td>16.0</td>\n",
              "      <td>6.385098</td>\n",
              "      <td>-159.680165</td>\n",
              "      <td>4</td>\n",
              "      <td>-6.7</td>\n",
              "      <td>16.1</td>\n",
              "      <td>24.1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>id3504673</td>\n",
              "      <td>2</td>\n",
              "      <td>2016-04-06 19:32:31</td>\n",
              "      <td>2016-04-06 19:39:40</td>\n",
              "      <td>1</td>\n",
              "      <td>-74.010040</td>\n",
              "      <td>40.719971</td>\n",
              "      <td>-74.012268</td>\n",
              "      <td>40.706718</td>\n",
              "      <td>N</td>\n",
              "      <td>...</td>\n",
              "      <td>235.8</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1.485498</td>\n",
              "      <td>-172.737700</td>\n",
              "      <td>4</td>\n",
              "      <td>7.2</td>\n",
              "      <td>16.1</td>\n",
              "      <td>25.9</td>\n",
              "      <td>0.0</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>id2181028</td>\n",
              "      <td>2</td>\n",
              "      <td>2016-03-26 13:30:55</td>\n",
              "      <td>2016-03-26 13:38:10</td>\n",
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              "      <td>40.793209</td>\n",
              "      <td>-73.972923</td>\n",
              "      <td>40.782520</td>\n",
              "      <td>N</td>\n",
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              "      <td>179.473585</td>\n",
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              "      <td>-73.994911</td>\n",
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              "      <td>311.7</td>\n",
              "      <td>8.0</td>\n",
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              "      <td>-119.059338</td>\n",
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              "      <td>7.8</td>\n",
              "      <td>16.1</td>\n",
              "      <td>11.1</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>1458640</th>\n",
              "      <td>id1049543</td>\n",
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              "      <td>2016-01-10 07:35:15</td>\n",
              "      <td>2016-01-10 07:46:10</td>\n",
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              "      <td>40.747379</td>\n",
              "      <td>-73.970184</td>\n",
              "      <td>40.796547</td>\n",
              "      <td>N</td>\n",
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              "      <td>589.6</td>\n",
              "      <td>11.0</td>\n",
              "      <td>6.049836</td>\n",
              "      <td>25.342196</td>\n",
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              "      <th>1458641</th>\n",
              "      <td>id2304944</td>\n",
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              "      <td>2016-04-22 07:10:25</td>\n",
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              "      <td>40.768799</td>\n",
              "      <td>-74.004433</td>\n",
              "      <td>40.707371</td>\n",
              "      <td>N</td>\n",
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              "      <td>642.9</td>\n",
              "      <td>10.0</td>\n",
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              "      <td>-150.788492</td>\n",
              "      <td>4</td>\n",
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              "      <td>16.1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <th>1458642</th>\n",
              "      <td>id2714485</td>\n",
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              "      <td>2016-01-05 15:56:26</td>\n",
              "      <td>2016-01-05 16:02:39</td>\n",
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              "      <td>-73.982079</td>\n",
              "      <td>40.749062</td>\n",
              "      <td>-73.974632</td>\n",
              "      <td>40.757107</td>\n",
              "      <td>N</td>\n",
              "      <td>...</td>\n",
              "      <td>161.6</td>\n",
              "      <td>7.0</td>\n",
              "      <td>1.092564</td>\n",
              "      <td>35.033294</td>\n",
              "      <td>0</td>\n",
              "      <td>-2.8</td>\n",
              "      <td>16.1</td>\n",
              "      <td>9.3</td>\n",
              "      <td>0.0</td>\n",
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              "      <th>1458643</th>\n",
              "      <td>id1209952</td>\n",
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              "      <td>2016-04-05 14:47:43</td>\n",
              "      <td>1</td>\n",
              "      <td>-73.979538</td>\n",
              "      <td>40.781750</td>\n",
              "      <td>-73.972809</td>\n",
              "      <td>40.790585</td>\n",
              "      <td>N</td>\n",
              "      <td>...</td>\n",
              "      <td>90.7</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.134042</td>\n",
              "      <td>29.969486</td>\n",
              "      <td>9</td>\n",
              "      <td>2.2</td>\n",
              "      <td>16.1</td>\n",
              "      <td>16.7</td>\n",
              "      <td>0.0</td>\n",
              "      <td>None</td>\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "taxi_data"
            }
          },
          "metadata": {},
          "execution_count": 110
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pEEXbpTfkBLo",
        "tags": []
      },
      "source": [
        "## 3. Разведывательный анализ данных (EDA)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "59de6wa-kBLo"
      },
      "source": [
        "В этой части нашего проекта мы с вами:\n",
        "* Исследуем сформированный набор данных;\n",
        "* Попробуем найти закономерности, позволяющие сформулировать предварительные гипотезы относительно того, какие факторы являются решающими в определении длительности поездки;\n",
        "* Дополним наш анализ визуализациями, иллюстрирующими; исследование. Постарайтесь оформлять диаграммы с душой, а не «для галочки»: навыки визуализации полученных выводов обязательно пригодятся вам в будущем.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d-8PQtqRkBLo"
      },
      "source": [
        "Начинаем с целевого признака. Забегая вперед, скажем, что основной метрикой качества решения поставленной задачи будет RMSLE - Root Mean Squared Log Error, которая вычисляется на основе целевой переменной в логарифмическом масштабе. В таком случае целесообразно сразу логарифмировать признак длительности поездки и рассматривать при анализе логарифм в качестве целевого признака:\n",
        "$$trip\\_duration\\_log = log(trip\\_duration+1),$$\n",
        "где под символом log подразумевается натуральный логарифм.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 111,
      "metadata": {
        "id": "y59BwN3MkBLp",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 739
        },
        "outputId": "d721539a-5907-49d9-f366-47e77bac38e6"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-111-6c22ac72038f>:1: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  taxi_data['trip_duration_log'] = np.log(taxi_data['trip_duration']+1)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                id  vendor_id     pickup_datetime     dropoff_datetime  \\\n",
              "0        id2875421          2 2016-03-14 17:24:55  2016-03-14 17:32:30   \n",
              "1        id2377394          1 2016-06-12 00:43:35  2016-06-12 00:54:38   \n",
              "2        id3858529          2 2016-01-19 11:35:24  2016-01-19 12:10:48   \n",
              "3        id3504673          2 2016-04-06 19:32:31  2016-04-06 19:39:40   \n",
              "4        id2181028          2 2016-03-26 13:30:55  2016-03-26 13:38:10   \n",
              "...            ...        ...                 ...                  ...   \n",
              "1458639  id2376096          2 2016-04-08 13:31:04  2016-04-08 13:44:02   \n",
              "1458640  id1049543          1 2016-01-10 07:35:15  2016-01-10 07:46:10   \n",
              "1458641  id2304944          2 2016-04-22 06:57:41  2016-04-22 07:10:25   \n",
              "1458642  id2714485          1 2016-01-05 15:56:26  2016-01-05 16:02:39   \n",
              "1458643  id1209952          1 2016-04-05 14:44:25  2016-04-05 14:47:43   \n",
              "\n",
              "         passenger_count  pickup_longitude  pickup_latitude  \\\n",
              "0                      1        -73.982155        40.767937   \n",
              "1                      1        -73.980415        40.738564   \n",
              "2                      1        -73.979027        40.763939   \n",
              "3                      1        -74.010040        40.719971   \n",
              "4                      1        -73.973053        40.793209   \n",
              "...                  ...               ...              ...   \n",
              "1458639                4        -73.982201        40.745522   \n",
              "1458640                1        -74.000946        40.747379   \n",
              "1458641                1        -73.959129        40.768799   \n",
              "1458642                1        -73.982079        40.749062   \n",
              "1458643                1        -73.979538        40.781750   \n",
              "\n",
              "         dropoff_longitude  dropoff_latitude store_and_fwd_flag  ...  \\\n",
              "0               -73.964630         40.765602                  N  ...   \n",
              "1               -73.999481         40.731152                  N  ...   \n",
              "2               -74.005333         40.710087                  N  ...   \n",
              "3               -74.012268         40.706718                  N  ...   \n",
              "4               -73.972923         40.782520                  N  ...   \n",
              "...                    ...               ...                ...  ...   \n",
              "1458639         -73.994911         40.740170                  N  ...   \n",
              "1458640         -73.970184         40.796547                  N  ...   \n",
              "1458641         -74.004433         40.707371                  N  ...   \n",
              "1458642         -73.974632         40.757107                  N  ...   \n",
              "1458643         -73.972809         40.790585                  N  ...   \n",
              "\n",
              "         number_of_steps haversine_distance   direction  geo_cluster  \\\n",
              "0                    5.0           1.498521   99.970196            9   \n",
              "1                    6.0           1.805507 -117.153768            4   \n",
              "2                   16.0           6.385098 -159.680165            4   \n",
              "3                    4.0           1.485498 -172.737700            4   \n",
              "4                    5.0           1.188588  179.473585            9   \n",
              "...                  ...                ...         ...          ...   \n",
              "1458639              8.0           1.225080 -119.059338            0   \n",
              "1458640             11.0           6.049836   25.342196            9   \n",
              "1458641             10.0           7.824606 -150.788492            4   \n",
              "1458642              7.0           1.092564   35.033294            0   \n",
              "1458643              2.0           1.134042   29.969486            9   \n",
              "\n",
              "        temperature visibility wind speed  precip  events  trip_duration_log  \n",
              "0               4.4        8.0       27.8     0.3    None           6.122493  \n",
              "1              28.9       16.1        7.4     0.0    None           6.498282  \n",
              "2              -6.7       16.1       24.1     0.0    None           7.661527  \n",
              "3               7.2       16.1       25.9     0.0    None           6.063785  \n",
              "4               9.4       16.1        9.3     0.0    None           6.077642  \n",
              "...             ...        ...        ...     ...     ...                ...  \n",
              "1458639         7.8       16.1       11.1     0.0    None           6.658011  \n",
              "1458640         7.2        2.8       18.5     8.1    Rain           6.486161  \n",
              "1458641        18.3       16.1        0.0     0.0    None           6.639876  \n",
              "1458642        -2.8       16.1        9.3     0.0    None           5.924256  \n",
              "1458643         2.2       16.1       16.7     0.0    None           5.293305  \n",
              "\n",
              "[1458233 rows x 30 columns]"
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              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>vendor_id</th>\n",
              "      <th>pickup_datetime</th>\n",
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              "      <th>dropoff_latitude</th>\n",
              "      <th>store_and_fwd_flag</th>\n",
              "      <th>...</th>\n",
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              "      <th>haversine_distance</th>\n",
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              "      <th>temperature</th>\n",
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              "      <th>wind speed</th>\n",
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              "      <td>40.767937</td>\n",
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              "      <td>40.765602</td>\n",
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              "      <td>99.970196</td>\n",
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              "      <td>8.0</td>\n",
              "      <td>27.8</td>\n",
              "      <td>0.3</td>\n",
              "      <td>None</td>\n",
              "      <td>6.122493</td>\n",
              "    </tr>\n",
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              "      <th>1</th>\n",
              "      <td>id2377394</td>\n",
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              "      <td>40.738564</td>\n",
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              "      <td>40.731152</td>\n",
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              "      <td>6.0</td>\n",
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              "      <td>-117.153768</td>\n",
              "      <td>4</td>\n",
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              "      <td>16.1</td>\n",
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              "      <th>2</th>\n",
              "      <td>id3858529</td>\n",
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              "      <td>2016-01-19 12:10:48</td>\n",
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              "      <td>40.763939</td>\n",
              "      <td>-74.005333</td>\n",
              "      <td>40.710087</td>\n",
              "      <td>N</td>\n",
              "      <td>...</td>\n",
              "      <td>16.0</td>\n",
              "      <td>6.385098</td>\n",
              "      <td>-159.680165</td>\n",
              "      <td>4</td>\n",
              "      <td>-6.7</td>\n",
              "      <td>16.1</td>\n",
              "      <td>24.1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>None</td>\n",
              "      <td>7.661527</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>id3504673</td>\n",
              "      <td>2</td>\n",
              "      <td>2016-04-06 19:32:31</td>\n",
              "      <td>2016-04-06 19:39:40</td>\n",
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              "      <td>6.063785</td>\n",
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              "      <th>4</th>\n",
              "      <td>id2181028</td>\n",
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              "      <td>-73.973053</td>\n",
              "      <td>40.793209</td>\n",
              "      <td>-73.972923</td>\n",
              "      <td>40.782520</td>\n",
              "      <td>N</td>\n",
              "      <td>...</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.188588</td>\n",
              "      <td>179.473585</td>\n",
              "      <td>9</td>\n",
              "      <td>9.4</td>\n",
              "      <td>16.1</td>\n",
              "      <td>9.3</td>\n",
              "      <td>0.0</td>\n",
              "      <td>None</td>\n",
              "      <td>6.077642</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1458639</th>\n",
              "      <td>id2376096</td>\n",
              "      <td>2</td>\n",
              "      <td>2016-04-08 13:31:04</td>\n",
              "      <td>2016-04-08 13:44:02</td>\n",
              "      <td>4</td>\n",
              "      <td>-73.982201</td>\n",
              "      <td>40.745522</td>\n",
              "      <td>-73.994911</td>\n",
              "      <td>40.740170</td>\n",
              "      <td>N</td>\n",
              "      <td>...</td>\n",
              "      <td>8.0</td>\n",
              "      <td>1.225080</td>\n",
              "      <td>-119.059338</td>\n",
              "      <td>0</td>\n",
              "      <td>7.8</td>\n",
              "      <td>16.1</td>\n",
              "      <td>11.1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>None</td>\n",
              "      <td>6.658011</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1458640</th>\n",
              "      <td>id1049543</td>\n",
              "      <td>1</td>\n",
              "      <td>2016-01-10 07:35:15</td>\n",
              "      <td>2016-01-10 07:46:10</td>\n",
              "      <td>1</td>\n",
              "      <td>-74.000946</td>\n",
              "      <td>40.747379</td>\n",
              "      <td>-73.970184</td>\n",
              "      <td>40.796547</td>\n",
              "      <td>N</td>\n",
              "      <td>...</td>\n",
              "      <td>11.0</td>\n",
              "      <td>6.049836</td>\n",
              "      <td>25.342196</td>\n",
              "      <td>9</td>\n",
              "      <td>7.2</td>\n",
              "      <td>2.8</td>\n",
              "      <td>18.5</td>\n",
              "      <td>8.1</td>\n",
              "      <td>Rain</td>\n",
              "      <td>6.486161</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1458641</th>\n",
              "      <td>id2304944</td>\n",
              "      <td>2</td>\n",
              "      <td>2016-04-22 06:57:41</td>\n",
              "      <td>2016-04-22 07:10:25</td>\n",
              "      <td>1</td>\n",
              "      <td>-73.959129</td>\n",
              "      <td>40.768799</td>\n",
              "      <td>-74.004433</td>\n",
              "      <td>40.707371</td>\n",
              "      <td>N</td>\n",
              "      <td>...</td>\n",
              "      <td>10.0</td>\n",
              "      <td>7.824606</td>\n",
              "      <td>-150.788492</td>\n",
              "      <td>4</td>\n",
              "      <td>18.3</td>\n",
              "      <td>16.1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>None</td>\n",
              "      <td>6.639876</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1458642</th>\n",
              "      <td>id2714485</td>\n",
              "      <td>1</td>\n",
              "      <td>2016-01-05 15:56:26</td>\n",
              "      <td>2016-01-05 16:02:39</td>\n",
              "      <td>1</td>\n",
              "      <td>-73.982079</td>\n",
              "      <td>40.749062</td>\n",
              "      <td>-73.974632</td>\n",
              "      <td>40.757107</td>\n",
              "      <td>N</td>\n",
              "      <td>...</td>\n",
              "      <td>7.0</td>\n",
              "      <td>1.092564</td>\n",
              "      <td>35.033294</td>\n",
              "      <td>0</td>\n",
              "      <td>-2.8</td>\n",
              "      <td>16.1</td>\n",
              "      <td>9.3</td>\n",
              "      <td>0.0</td>\n",
              "      <td>None</td>\n",
              "      <td>5.924256</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1458643</th>\n",
              "      <td>id1209952</td>\n",
              "      <td>1</td>\n",
              "      <td>2016-04-05 14:44:25</td>\n",
              "      <td>2016-04-05 14:47:43</td>\n",
              "      <td>1</td>\n",
              "      <td>-73.979538</td>\n",
              "      <td>40.781750</td>\n",
              "      <td>-73.972809</td>\n",
              "      <td>40.790585</td>\n",
              "      <td>N</td>\n",
              "      <td>...</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.134042</td>\n",
              "      <td>29.969486</td>\n",
              "      <td>9</td>\n",
              "      <td>2.2</td>\n",
              "      <td>16.1</td>\n",
              "      <td>16.7</td>\n",
              "      <td>0.0</td>\n",
              "      <td>None</td>\n",
              "      <td>5.293305</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1458233 rows × 30 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-64425cdf-54ca-4237-8676-71cbb802b049')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
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              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-64425cdf-54ca-4237-8676-71cbb802b049 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-64425cdf-54ca-4237-8676-71cbb802b049');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
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              "\n",
              "\n",
              "<div id=\"df-9d5c33d7-d6aa-4240-82d1-3b7e3d5b14ec\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-9d5c33d7-d6aa-4240-82d1-3b7e3d5b14ec')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
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              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-9d5c33d7-d6aa-4240-82d1-3b7e3d5b14ec button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "taxi_data"
            }
          },
          "metadata": {},
          "execution_count": 111
        }
      ],
      "source": [
        "taxi_data['trip_duration_log'] = np.log(taxi_data['trip_duration']+1)\n",
        "taxi_data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OoqAWXLLkBLp"
      },
      "source": [
        "### Задание 3.1.\n",
        "Постройте гистограмму и коробчатую диаграмму длительности поездок в логарифмическом масштабе (trip_duration_log).\n",
        "Исходя из визуализации, сделайте предположение, является ли полученное распределение нормальным?\n",
        "Проверьте свою гипотезу с помощью теста Д’Агостино при уровне значимости $\\alpha=0.05$.\n",
        "\n",
        "а) Чему равен вычисленный p-value? Ответ округлите до сотых.\n",
        "\n",
        "б) Является ли распределение длительности поездок в логарифмическом масштабе нормальным?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 112,
      "metadata": {
        "_uuid": "4cea6d1f4372850e6c5692d7be7ca1cee3bdb42e",
        "id": "hSPVCtNckBLp",
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 410
        },
        "outputId": "43d96572-ce41-4fcc-8f4a-565935e8fe7f"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x400 with 2 Axes>"
            ],
            "image/png": 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Oaq0AKrzyWvgr9xxzuZGYQ0W0aNEiq0MoV0eOHKmwSa5nnnlGjRo1sjqMclXVksfp6en6/fffze3k5GSFhoZaGBEupETJudtvv91t2zAM/f7779q1a5eee+65UgkMAAAAsEJZL/w1bdo0c2jrrl273Iay7tq1y61ecfTs2dNtaGvPnj3PW784C0VcqG55Tp5d0LlhvSuvvNLqEFBEjRo1qnK/r4cffrjKDG318vLSjh07tH//frPshx9+0JVXXik/P78Ch7babDaGtlqsRMm53BMLSn/+8ps2baopU6aoR48epRIYAKDiMQxDdrtdgYGBTCwLAIXo2LGj+fPjjz8uqeChrLnrFSb3fGsOh+OCc8blnZ/tQvWLUrco+0mgVW4s9gFPd9ddd1WZ5FzLli319ddfu5VlZ2erd+/ehT6HxFx+5f25VqLk3FtvvVXacQAAKgGHw6H7F2/Vu49EM7wVAM4jb5Isb2KuOH8UXGhBhOIs/JC3flHqknir2kjMoaKoKp9X33//vdUhVHhWfK5d1Jxzu3fvNrtKtmjRQm3bti2VoAAAFZePX+nNPQoAldmmTZu0detWt9Vbp02bVqQecwUdq6A/Ogv7A6M49S9U90L7C9tXFf5IruxIzKGi2bRpk1auXOkxvei8vLzk4+OjnJwcs/eaj4+PsrOzZRiGbDabvL29ZbPZFBgYKH9/fwUEBCgrK0tpaWmSpCZNmujOO+/UkSNHtG3bNkl/9ry+7bbbdODAAaWkpKhmzZrKzMzU+++/r3PnzunKK69U+/bttWzZMiUnJysgIECBgYHy8vKSr6+vgoKCdPz4cWVkZKh69eoKCwtTYmKikpKSzFh8fHyUnp6u9PR0GYYhX19f81pq1aqlLl26aNOmTUpJSVFmZqZ5PUFBQfL29paPj49CQkIUGBio48ePKzs7W/7+/rrsssvk5eWls2fPKi0tTSEhITp79qxOnjwpSfL29pa/v7/sdrvS09Pdhul6eXm53cegoCClp6crIyOjyL0DrfpcK1Fy7sSJExowYIA2bdqkmjVrSpJOnz6tG2+8Ue+8847q1q1bmjECAAAAlVLHjh1L7Q+B4h6nuL3zSrr/fAlCVDw//fSTYmJiqtwCEKg87rrrLrVp08Z8HVeW+fduuOEG3XvvvW5leTtQRUVFuW136dKlTGOqagtxXAyvkjxp9OjROnv2rPbt26eUlBSlpKRo7969Sk1N1SOPPFLaMQIAKgDDMORwOKwOAwAAAAAqlBL1nFu9erXWrVunZs2amWXNmzfX/PnzWRACAKooZ1amRi/bqcDgWlaHAgAAAAAVRol6zjmdTvn6+uYr9/X1ldPpvOigAAAVk7efv9UhAAAAAECFUqLk3E033aQxY8bot99+M8uOHz+uRx99VN26dSu14ADAUxiGIbvdLqfTKbvdLknKyczgCwkAAAAAwEUpUXJu3rx5Sk1NVePGjdWkSRM1adJEkZGRSk1N1dy5c0s7RgCwjCspZ7fbddfs1Tp+/LhiFm+VM4ekHAAAAADg4pVozrkGDRro22+/1bp163TgwAFJUrNmzdS9e/dSDQ4ArOZwOHTX7NV6bWB72Ww2jV66Td6B1a0OCwAAAABQSRSr59yGDRvUvHlzpaamymaz6eabb9bo0aM1evRoXXvttWrRooW+/PLLsooVACxhs9k04q/ecl6+zKkGAAAAACg9xUrOxcbGavjw4QoODs63LyQkRCNGjNCsWbNKLTgA8BReLHQAAAAAACgDxUrOfffdd7rlllsK3d+jRw/t3r37ooMCAE9ht9uZX66YXPP0GYZhdSgAAAAA4PGKlZxLSkqSr69voft9fHz0xx9/XHRQAOAJzCST1YFUMDlZGRq6cLMcDofVoQAAAACAxytWcu6SSy7R3r17C93//fffq379+hcdFAB4AofDoeGLNspw0nOuuLz9AqwOAQAAAAAqhGIl53r16qXnnntO6enp+fY5HA49//zz6tOnT6kFBwDlzW63m0My7Xa7vJlrDgAAAABQhnyKU/nZZ5/VBx98oCuvvFKjRo1S06ZNJUkHDhzQ/PnzlZOTo2eeeaZMAgWA8mIYhpKTkzX4tXVy5jhl8/G2OqQKJzszXXa7XdWqVbM6FAAAAADwaMVKzoWFhWnbtm166KGHNGHCBHOyb5vNpujoaM2fP19hYWFlEigAlBeHw6HBr62TzdtXysmwOhwAAAAAQCVWrOScJDVq1Eiff/65Tp06pV9++UWGYeiKK65QrVq1yiI+ALCEt58/q7RehNwrttpsNqvDAQAAAACPVaw553KrVauWrr32Wl133XUk5gAAbpxZmRqxdAcrtgIAAADABZQ4OQcAwPmwmAYAAAAAXBjJOQAAAAAAAMAixZ5zDgAqI8Mw5HA4zIVuAAAAAAAoD/ScAwD9uULr3XPWMEcaAAAAAKBckZwDgL/4+AVYHQIAAAAAoIohOQcAAAAAAABYhOQcAAAAAAAAYBGScwCAMmEYhux2O4tsAAAAAMB5eHxy7vjx47rvvvtUu3ZtBQYGqlWrVtq1a5e53zAMTZw4UfXr11dgYKC6d++un3/+2e0YKSkpGjhwoIKDg1WzZk0NGzZM586dc6vz/fffq1OnTgoICFCDBg00Y8aMcrk+AKisnFmZGhK3ScnJySToAAAAAKAQHp2cO3XqlG644Qb5+vrqiy++0I8//qiZM2eqVq1aZp0ZM2Zozpw5iouL044dOxQUFKTo6Gilp6ebdQYOHKh9+/YpPj5en376qbZs2aKYmBhzf2pqqnr06KFGjRpp9+7devnllzVp0iQtWrSoXK8XACodm01DF25mFVwAAAAAKISP1QGcz0svvaQGDRrorbfeMssiIyPNnw3DUGxsrJ599lnddtttkqS3335bYWFhWrVqlQYMGKD9+/dr9erV2rlzp9q3by9Jmjt3rnr16qVXXnlFERERWr58uTIzM/Xmm2/Kz89PLVq0UEJCgmbNmuWWxAMAFJ83q+ACAAAAQKE8uufcxx9/rPbt26t///6qV6+e2rZtq9dff93cf+jQISUmJqp79+5mWUhIiDp06KDt27dLkrZv366aNWuaiTlJ6t69u7y8vLRjxw6zTufOneXn52fWiY6O1sGDB3Xq1KkCY8vIyFBqaqrbAwCQH3PPAQAAAEDhPDo597///U8LFizQFVdcoTVr1uihhx7SI488oqVLl0qSEhMTJUlhYWFuzwsLCzP3JSYmql69em77fXx8FBoa6lanoGPkPkde06dPV0hIiPlo0KDBRV4tAFROOVkZDG0FAAAAgEJ4dHLO6XTqmmuu0Ysvvqi2bdsqJiZGw4cPV1xcnNWhacKECTpz5oz5OHbsmNUhAYDHYmgrAAAAABTMo5Nz9evXV/Pmzd3KmjVrpqNHj0qSwsPDJUlJSUludZKSksx94eHhOnHihNv+7OxspaSkuNUp6Bi5z5GXv7+/goOD3R4AAAAAAABAcXh0cu6GG27QwYMH3cp++uknNWrUSNKfi0OEh4dr/fr15v7U1FTt2LFDUVFRkqSoqCidPn1au3fvNuts2LBBTqdTHTp0MOts2bJFWVlZZp34+Hg1bdrUbWVYAAAAAAAAoDR5dHLu0Ucf1ddff60XX3xRv/zyi1asWKFFixZp5MiRkiSbzaaxY8dq2rRp+vjjj/XDDz9o8ODBioiIUL9+/ST92dPulltu0fDhw/XNN9/oq6++0qhRozRgwABFRERIku699175+flp2LBh2rdvn959913Nnj1b48aNs+rSAQAAAAAAUAX4WB3A+Vx77bX68MMPNWHCBE2ZMkWRkZGKjY3VwIEDzTpPPPGE0tLSFBMTo9OnT6tjx45avXq1AgL+nt9o+fLlGjVqlLp16yYvLy/dcccdmjNnjrk/JCREa9eu1ciRI9WuXTvVqVNHEydOVExMTLleLwBUVq4VWwMDA2Wz2awOBwAAAAA8hkcn5ySpT58+6tOnT6H7bTabpkyZoilTphRaJzQ0VCtWrDjvea6++mp9+eWXJY4TAFA414qtKx/tqWrVqlkdDgAAAAB4DI8e1goAqBxyMjMkH1+rwwAAAAAAj0NyDgAAAAAAALAIyTkAAAAAAADAIh4/5xwAlCfDMORwOKwOAwAAAABQRdBzDgBycTgcGrF4q5w5TqtDAQAAAABUASTnAOAvrl5zNj9/q0OptAzDkN1ul2EYVocCAAAAAB6B5BwA/CUnK0Ojl26T4aTXXFlxOBy6e84ahg4DAAAAwF9IzgFALl6+9Joraz5+AVaHAAAAAAAeg+QcAAAAAAAAYBGScwCAcuGabw4AAAAA8DeScwCAcuHMymQlXAAAAADIg+QcAKDceLESLgAAAAC4ITkHoEozDENpaWlKS0uzOpQqwzW81TAMq0MBAAAAAMuRnANQpTkcDvWf+YkGz4tnuGU5ycnK0NCFm+VwOKwOBQAAAAAs52N1AABgBVfvLXOBAh9fawOqYrz9AqwOAQAAAAA8Ask5AFWSq8eckeOUIcnm4211SAAAAACAKohhrQCqHFevOW8/fxYoAAAAAABYip5zAKoUwzB07NgxxSzeKm8ScwAAAAAAi9FzDkCV4nA4NHzRRtmYYw4AAAAA4AFIzgGocugxBwAAAADwFCTnAFQZrrnmAAAAUDINGzbUokWL1LBhQ6tDAUqM1zE8DXPOAagyHA6HBr+2Ts4cJ6uzWiw7M112u13VqlWzOhQAAFAMAQEBuvLKK60OA7govI7haeg5B6BKYUgrAAAAAMCTkJwDAJQ7wzCUlpamtLQ0GYZhdTgAAAAAYBmScwAqvbS0NJ08eZIkkAdxZmVq+Btf6u7Y1XI4HFaHAwAAAACWITkHoNJzOBwaunAzSSAP4+3nL2+/AKvDAAAAAABLkZwDUCWQBAIAAAAAeCKScwAAAAAAAIBFfKwOAADKimEYcjgczDUHAAAAAPBYFarn3L///W/ZbDaNHTvWLEtPT9fIkSNVu3ZtVa9eXXfccYeSkpLcnnf06FH17t1b1apVU7169TR+/HhlZ2e71dm0aZOuueYa+fv76/LLL9eSJUvK4YoAlCWHw6G756xhrjkAAAAAgMeqMMm5nTt3auHChbr66qvdyh999FF98skneu+997R582b99ttvuv322839OTk56t27tzIzM7Vt2zYtXbpUS5Ys0cSJE806hw4dUu/evXXjjTcqISFBY8eO1QMPPKA1a9aU2/UBKBs+zDXn0QzDkN1up3cjAAAAgCqrQiTnzp07p4EDB+r1119XrVq1zPIzZ85o8eLFmjVrlm666Sa1a9dOb731lrZt26avv/5akrR27Vr9+OOPWrZsmdq0aaOePXtq6tSpmj9/vjIzMyVJcXFxioyM1MyZM9WsWTONGjVKd955p1599dVCY8rIyFBqaqrbAwBQPDlZGRoSt0nJyckk6AAAAABUSRUiOTdy5Ej17t1b3bt3dyvfvXu3srKy3MqvuuoqNWzYUNu3b5ckbd++Xa1atVJYWJhZJzo6Wqmpqdq3b59ZJ++xo6OjzWMUZPr06QoJCTEfDRo0uOjrBFA67Ha77Ha71WGgqGw2DV24meHHAAAAAKokj0/OvfPOO/r22281ffr0fPsSExPl5+enmjVrupWHhYUpMTHRrJM7Mefa79p3vjqpqamF/rE4YcIEnTlzxnwcO3asRNcHAJC8GX4MAAAAoIry6NVajx07pjFjxig+Pl4BAZ71h5u/v7/8/f2tDgMAAAAAAAAVmEf3nNu9e7dOnDiha665Rj4+PvLx8dHmzZs1Z84c+fj4KCwsTJmZmTp9+rTb85KSkhQeHi5JCg8Pz7d6q2v7QnWCg4MVGBhYRlcHAAAAAACAqs6jk3PdunXTDz/8oISEBPPRvn17DRw40PzZ19dX69evN59z8OBBHT16VFFRUZKkqKgo/fDDDzpx4oRZJz4+XsHBwWrevLlZJ/cxXHVcxwAAAAAAAADKgkcPa61Ro4ZatmzpVhYUFKTatWub5cOGDdO4ceMUGhqq4OBgjR49WlFRUfrHP/4hSerRo4eaN2+uQYMGacaMGUpMTNSzzz6rkSNHmsNSH3zwQc2bN09PPPGE7r//fm3YsEErV67UZ599Vr4XDABVVFaGQydPnlSDBg1ks9msDgcAAAAAyo1H95wrildffVV9+vTRHXfcoc6dOys8PFwffPCBud/b21uffvqpvL29FRUVpfvuu0+DBw/WlClTzDqRkZH67LPPFB8fr9atW2vmzJl64403FB0dbcUlAUCV48zK1IilO1ixFQAAAECV49E95wqyadMmt+2AgADNnz9f8+fPL/Q5jRo10ueff37e43bt2lV79uwpjRABACXg7cciOwAAAACqngrfcw4AAAAAAACoqEjOAQAAAAAAABYhOQcAAAAAAABYhOQcAMAjGIahtLQ0paWlyTAMq8MBAAAAgHJBcg4A4BGcWZkaPC9ed8euZtVWAAAAAFUGyTkAgMfw8vOXt1+A1WEAAAAAQLkhOQcAAAAAAABYhOQcAAAAAAAAYBGScwAqNcMw5HA45DQM2e12q8MBAAAAAMANyTkAlVpOVoZGL92m7AyHRizeKmeO0+qQAAAAAAAwkZwDUOl5+fr/+a+fv8WRAAAAAADgjuQcAMCjZGU4dPLkSRmGYXUoAAAAAFDmSM4BqFSMv+aWczqdzDFXQTmzMhWz5GslJyeToAMAAABQ6ZGcA1CpOBwODZofr+PHj2vwa+uYY66CstlsGrpwsxwOh9WhAAAAAECZIjkHoFJw9ZgzDEM2m02jl+2UzdvX6rBwEbz9AqwOAQAAAADKHMk5AJWCw+HQ3XPWmD2tvFn8AQAAAABQAZCcA1BpePv6M89cJZK7NyQAAAAAVFYk5wBUaLkTODlZGRqxeCvzzFUSOVkZzDsHAAAAoNIjOQegQnM4HLpr9mr9+uuvyslxyovhrJUK884BAAAAqOxIzgGo8Gw2m0Yv3SbDSY+5yoahrQAAAAAqO5JzACosV+JGkrx86TFXGeVkZWjQ3LVKTk62OhQAAAAAKBMk5wBUWA6HQ4NfW8ccc5UcQ5UBAAAAVGYk5wBUSK5ec94kbgAAAAAAFRjJOQAVjmEYSk5OptdcFcG8cwAAAAAqM5JzACoc13BWm7ev1aGgHDizMhWz5GslJyeToAMAAABQ6ZCcA1AhMZy1arHZbBq6cLMcDofVoQAAAABAqSI5B6BCyb1CK6oWL19/hrcCAAAAqHRIzgGoUFihterKycqg9xwAAACASsejk3PTp0/Xtddeqxo1aqhevXrq16+fDh486FYnPT1dI0eOVO3atVW9enXdcccdSkpKcqtz9OhR9e7dW9WqVVO9evU0fvx4ZWdnu9XZtGmTrrnmGvn7++vyyy/XkiVLyvryABRD7kUBGNJaddF7DgAAAEBl49HJuc2bN2vkyJH6+uuvFR8fr6ysLPXo0UNpaWlmnUcffVSffPKJ3nvvPW3evFm//fabbr/9dnN/Tk6OevfurczMTG3btk1Lly7VkiVLNHHiRLPOoUOH1Lt3b914441KSEjQ2LFj9cADD2jNmjXler0ACudwOHT3nDX0mqri6D0HAAAAoLLxsTqA81m9erXb9pIlS1SvXj3t3r1bnTt31pkzZ7R48WKtWLFCN910kyTprbfeUrNmzfT111/rH//4h9auXasff/xR69atU1hYmNq0aaOpU6fqySef1KRJk+Tn56e4uDhFRkZq5syZkqRmzZpp69atevXVVxUdHV3u1w2gYD5+AVaHAA/gzesAAAAAQCXi0T3n8jpz5owkKTQ0VJK0e/duZWVlqXv37madq666Sg0bNtT27dslSdu3b1erVq0UFhZm1omOjlZqaqr27dtn1sl9DFcd1zEKkpGRodTUVLcHgLJjt9uZZw4AAAAAUOlUmOSc0+nU2LFjdcMNN6hly5aSpMTERPn5+almzZpudcPCwpSYmGjWyZ2Yc+137TtfndTU1EKHTk2fPl0hISHmo0GDBhd9jQCAC8s9/yAAAAAAVHQVJjk3cuRI7d27V++8847VoUiSJkyYoDNnzpiPY8eOWR0SUOmQhEFBMtNSNWjBRuadAwAAAFApVIjk3KhRo/Tpp59q48aNuvTSS83y8PBwZWZm6vTp0271k5KSFB4ebtbJu3qra/tCdYKDgxUYGFhgTP7+/goODnZ7AChdLAKBwnj5+pG4BQAAAFApeHRyzjAMjRo1Sh9++KE2bNigyMhIt/3t2rWTr6+v1q9fb5YdPHhQR48eVVRUlCQpKipKP/zwg06cOGHWiY+PV3BwsJo3b27WyX0MVx3XMQBYh0UgUBBnViartgIAAACoFDx6tdaRI0dqxYoV+uijj1SjRg1zjriQkBAFBgYqJCREw4YN07hx4xQaGqrg4GCNHj1aUVFR+sc//iFJ6tGjh5o3b65BgwZpxowZSkxM1LPPPquRI0fK399fkvTggw9q3rx5euKJJ3T//fdrw4YNWrlypT777DPLrh2o6ux2u+x2u9VhwIN5+frLbrcrMDBQNpvN6nAAAAAAoEQ8uufcggULdObMGXXt2lX169c3H++++65Z59VXX1WfPn10xx13qHPnzgoPD9cHH3xg7vf29tann34qb29vRUVF6b777tPgwYM1ZcoUs05kZKQ+++wzxcfHq3Xr1po5c6beeOMNRUdHl+v1AgCKLicrQ0PiNik5OZnhrQAAAAAqLI/uOVeUP7YCAgI0f/58zZ8/v9A6jRo10ueff37e43Tt2lV79uwpdowAAAvZbBq6cLNWPtpT1apVszoaAAAAACg2j+45B6BqYSgrSsKbeQkBAAAAVGAe3XMOAIALycpw6OTJk2rQoAFzz6HUGYYhh8PB3IYAgAJ5pZ8pm+M6Trv9WyrHLKNYAVw8knMAKgzDMMyedTmZGbL5+MrLiw7AVZ0zK1MxS77WsoerqXbt2iRQUKocDofunrNG7z4SzdBpAIApJCREvn7+0v82l+l5Ag9tKdXj+fr5KyQkpFSPCeDikZwDUGHkZGVoxOKt8vLztzoUeBgbc8+hDPkwdBoAkEdYWJiW/edtnTlTsXqjhYSEKCwszOowAORBcg5AhUJiDoXx8vWX3W5n+CEAACgXYWFhJLoAlArGgwHweK7hrBdevxlVWU5WhoYu3CyHw2F1KAAAAABQZCTnAHgkMyFnGEpOTtbwRRtlOJ1WhwUPx8qtAAAAACoaknMAPJLD4dCIxVuVlf7nvzZvX6tDQgWQO6kLAAAAABUByTkAHiNvYsU1vxzzzKGocrIyNCRuk5KTk0nQAQAAAKgQSM4B8BgOh0NDF26W3W5njjmUSE5mhpyGQYIOAAAAQIVBcg6AR/H2C5DD4WCOOVwcm43FIQAAAABUCCTnAFjKNZTV6XT++e9f294MZcVF8vL1Z/45AAAAAB6P5BwAy7hWYr1r9mr9+uuvGr5oo7Iz/lwAwplDrzlcnJysDHrPAQAAAPB4JOcAWMbhcGjwa+tkOA23FVlZAAKlxWkYOnnyJL3nAAAAAHgsknMAypVhGEpLS9O5c+eUlpZmDl8lIYey4MzKVMySr1kcAgAAAIDH8rE6AABVi8PhUP+Zn8jIcSonK0PegdXl5cX3BCg7NptNQ+I2aemDXVW7dm3ZbDarQwIAAAAAE38RAygXroUfDMOQt5+/vPz85eVLbzmUk78SdPSgAwAAAOBpSM4BKHOuhR/unrOGyflhHZuNBSIAAAAAeByScwBKVe4ecna7XXa73Vz4wZnjlN1utzpEVGEsEAEAAADA05CcA1CqHA6H7p6zxkzMOZ1/JuS8WfABHsCZlakRS3fQew4AAACAxyA5B6DU+fgFmL3lUlJSzF5zgCfw8vVTWlqa0tLS6EEHAAAAwHIk5wBcNNcQVlcvORdXbzl6zcGTOLMyNXhevO569QsWiAAAAABgOZJzAC6aaygrveRQUXj5+Us2m+6bs0bHjh0jQQcAAADAMiTnABSLay45V2+5tLQ02e12+fgFSDp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          },
          "metadata": {}
        }
      ],
      "source": [
        "fig, axes = plt.subplots(nrows = 1, ncols = 2, figsize = (15, 4))\n",
        "histplot = sns.histplot(data = taxi_data, x = 'trip_duration_log', ax = axes[0])\n",
        "histplot.set_title('Trip Duration Logarithmic Distribution')\n",
        "boxplot = sns.boxplot(data = taxi_data, x = 'trip_duration_log', ax = axes[1])\n",
        "boxplot.set_title('Trip Duration Logarithmic Boxplot');"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "alpha = 0.05 #уровень значимости\n",
        "\n",
        "# тест Д'Агостино (возвращает двустороннюю вероятность для проверки гипотезы)\n",
        "_, p = stats.normaltest(taxi_data['trip_duration_log'])\n",
        "print(f'P-value = {round(p, 2)}')"
      ],
      "metadata": {
        "id": "nd4G2dKOUqMl",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7c9841f3-bd03-425e-d68e-ace2cbb236a3"
      },
      "execution_count": 113,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "P-value = 0.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "if p > alpha / 2: # p-value рассчитано для двусторонней гипотезы, поэтому уровень значимости делим на 2\n",
        "    print(\"Нормальное распределение\")\n",
        "else:\n",
        "    print(\"Не нормальное распределение\")"
      ],
      "metadata": {
        "id": "a0IA6fAnU5js",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e8d2e5bf-31d5-4163-89b9-d968036a4466"
      },
      "execution_count": 114,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Не нормальное распределение\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "teZmcjlIkBLp"
      },
      "source": [
        "### Задание 3.2.\n",
        "Постройте визуализацию, которая позволит сравнить распределение длительности поездки в логарифмическом масштабе (trip_duration_log) в зависимости от таксопарка (vendor_id).\n",
        "\n",
        "Сравните два распределения между собой."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 115,
      "metadata": {
        "id": "SfyhOuOYkBLp",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 532
        },
        "outputId": "003b70e5-579d-414b-a1dc-79319db17431"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-115-e6f6bc937c6b>:1: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  taxi_data['vendor_id'] = taxi_data['vendor_id'].astype('category')\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Коробчатая диаграмма')"
            ]
          },
          "metadata": {},
          "execution_count": 115
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x400 with 2 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ],
      "source": [
        "taxi_data['vendor_id'] = taxi_data['vendor_id'].astype('category')\n",
        "\n",
        "fig, axes = plt.subplots(nrows = 2, ncols = 1, figsize = (15, 4))\n",
        "histplot = sns.histplot(\n",
        "    data = taxi_data,\n",
        "    x = 'trip_duration_log',\n",
        "    y = 'vendor_id',\n",
        "    ax = axes[0]\n",
        ")\n",
        "histplot.set_title('Зависимость длительности поездки в зависимости от вендора')\n",
        "\n",
        "boxplot = sns.boxplot(\n",
        "    data = taxi_data,\n",
        "    x = 'trip_duration_log',\n",
        "    y = 'vendor_id',\n",
        "    ax = axes[1]\n",
        ");\n",
        "boxplot.set_title('Коробчатая диаграмма')"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Можем сделать выводы, что вендор никак не влияет на продолжительность поездки"
      ],
      "metadata": {
        "id": "ONG6y-8nWyHm"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Gq7d2zjjkBLp"
      },
      "source": [
        "### Задание 3.3.\n",
        "Постройте визуализацию, которая позволит сравнить распределение длительности поездки в логарифмическом масштабе (trip_duration_log) в зависимости от признака отправки сообщения поставщику (store_and_fwd_flag).\n",
        "\n",
        "Сравните два распределения между собой."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 116,
      "metadata": {
        "id": "3If4bSdEkBLp",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 410
        },
        "outputId": "db2b9e7a-0bc6-4ce2-fdfa-9153f0a214ef"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x400 with 2 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ],
      "source": [
        "fig, axes = plt.subplots(nrows = 2, ncols = 1, figsize = (15, 4))\n",
        "\n",
        "histplot = sns.histplot(\n",
        "    data = taxi_data,\n",
        "    x = 'trip_duration_log',\n",
        "    y = 'store_and_fwd_flag',\n",
        "    ax = axes[0]\n",
        ")\n",
        "histplot.set_title('Распределение длительности поездки в зависимости от признака отправки сообщения поставщику')\n",
        "violinplot = sns.violinplot(\n",
        "    data = taxi_data,\n",
        "    x = 'trip_duration_log',\n",
        "    y = 'store_and_fwd_flag',\n",
        "    ax = axes[1]\n",
        ");"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Когда нет флага хранить и отправлять поездку, ее длительность более выраженная"
      ],
      "metadata": {
        "id": "0YAVkylgXp74"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ThgfBISekBLp"
      },
      "source": [
        "### Задание 3.4.\n",
        "Постройте две визуализации:\n",
        "* Распределение количества поездок в зависимости от часа дня;\n",
        "* Зависимость медианной длительности поездки от часа дня.\n",
        "\n",
        "На основе построенных графиков ответьте на следующие вопросы:\n",
        "\n",
        "а) В какое время суток такси заказывают реже всего?\n",
        "\n",
        "б) В какое время суток наблюдается пик медианной длительности поездок?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 117,
      "metadata": {
        "id": "b7tRWY_BkBLq",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 410
        },
        "outputId": "232fa402-84b8-4e77-9117-9292c5db29f2"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x400 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "duration_by_hour = taxi_data.groupby('pickup_hour')['trip_duration'].median()\n",
        "\n",
        "fig, axes = plt.subplots(nrows = 1, ncols = 2, figsize = (15, 4))\n",
        "countplot = sns.countplot(data = taxi_data, x = 'pickup_hour', ax = axes[0])\n",
        "countplot.set_title('Trip Duration Count by Hour')\n",
        "lineplot = sns.lineplot(data = duration_by_hour, ax = axes[1])\n",
        "lineplot.set_title('Trip Duration Median by Hour')\n",
        "lineplot.xaxis.set_ticks(duration_by_hour.index)\n",
        "lineplot.set_ylabel('median');"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Такси заказывают реже в ночное время суток (3, 4, 5 часов)\n",
        "\n",
        "Пик медианной длительности - дневные часы (13, 14, 15 часов)\n"
      ],
      "metadata": {
        "id": "xMJj3_24YIry"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GmHyGkbzkBLq"
      },
      "source": [
        "### Задание 3.5.\n",
        "Постройте две визуализации:\n",
        "* Распределение количества поездок в зависимости от дня недели;\n",
        "*  Зависимость медианной длительности поездки от дня недели.\n",
        "\n",
        "На основе построенных графиков ответьте на следующие вопросы:\n",
        "а) В какой день недели совершается больше всего поездок?\n",
        "б) В какой день недели медианная длительность поездок наименьшая?\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 118,
      "metadata": {
        "id": "b0mHfS17kBLq",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 410
        },
        "outputId": "e4788884-6fed-408a-b9dc-c012258b7426"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x400 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "duration_by_day = taxi_data.groupby('pickup_day_of_week')['trip_duration'].median()\n",
        "\n",
        "fig, axes = plt.subplots(nrows = 1, ncols = 2, figsize = (15, 4))\n",
        "countplot = sns.countplot(data = taxi_data, x = 'pickup_day_of_week', ax = axes[0])\n",
        "countplot.set_title('Trip Duration Count by Day of Week')\n",
        "lineplot = sns.lineplot(data = duration_by_day, ax = axes[1])\n",
        "lineplot.set_title('Trip Duration Median by Day of Week')\n",
        "lineplot.set_ylabel('median');"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Больше всего поездок происходит в пятницу (столбик 4)\n",
        "\n",
        "Медианная длительность поездок наименьшая в воскресенье"
      ],
      "metadata": {
        "id": "PxZQX9WsYoHh"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KSRSS4tekBLq"
      },
      "source": [
        "### Задание 3.6.\n",
        "Посмотрим на обе временные характеристики одновременно.\n",
        "\n",
        "Постройте сводную таблицу, по строкам которой отложены часы (pickup_hour), по столбцам - дни недели (pickup_day_of_week), а в ячейках - медианная длительность поездки (trip_duration).\n",
        "\n",
        "Визуализируйте полученную сводную таблицу с помощью тепловой карты (рекомендуемая палитра - coolwarm)."
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Сводная таблица:"
      ],
      "metadata": {
        "id": "Y4J8SWOrZCRo"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 119,
      "metadata": {
        "id": "zDUyBJTQkBLq"
      },
      "outputs": [],
      "source": [
        "pivot = taxi_data.pivot_table(\n",
        "    values = 'trip_duration',\n",
        "    index = 'pickup_hour',\n",
        "    columns = 'pickup_day_of_week',\n",
        "    aggfunc = 'median'\n",
        ")\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Визуализация с heatmap"
      ],
      "metadata": {
        "id": "HeTnstZrZHwu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "fig = plt.figure(figsize = (10, 7))\n",
        "heatmap = sns.heatmap(data = pivot, cmap = 'coolwarm')\n",
        "heatmap.set_title('Длительности поездки в зависимости от временных характеристих');\n"
      ],
      "metadata": {
        "id": "6-p51bnPZLWR",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 641
        },
        "outputId": "89eb2deb-b933-416e-e557-8b9c20ad9bc7"
      },
      "execution_count": 120,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x700 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Самые длительные поездки происходят в будние дни, преимущественно вторник, среда, четверг в рабочие часы (преимущественно с 9 до 16)"
      ],
      "metadata": {
        "id": "KvSYH10CZbEl"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E5P2WBT6kBLq",
        "tags": []
      },
      "source": [
        "### Задание 3.7.\n",
        "Постройте две диаграммы рассеяния (scatter-диаграммы):\n",
        "* первая должна иллюстрировать географическое расположение точек начала поездок (pickup_longitude, pickup_latitude)\n",
        "* вторая должна географическое расположение точек завершения поездок (dropoff_longitude, dropoff_latitude).\n",
        "\n",
        "Для этого на диаграммах по оси абсцисс отложите широту (longitude), а по оси ординат - долготу (latitude).\n",
        "Включите в визуализацию только те точки, которые находятся в пределах Нью-Йорка - добавьте следующие ограничения на границы осей абсцисс и ординат:\n",
        "\n",
        "city_long_border = (-74.03, -73.75)\n",
        "\n",
        "city_lat_border = (40.63, 40.85)\n",
        "\n",
        "Добавьте на диаграммы расцветку по десяти географическим кластерам (geo_cluster), которые мы сгенерировали ранее.\n",
        "\n",
        "**Рекомендация:** для наглядности уменьшите размер точек на диаграмме рассеяния.  \n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 121,
      "metadata": {
        "id": "T0Wd1q_PkBLq",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 787
        },
        "outputId": "ee016426-1e6e-409d-9da4-496ffb1a37af"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/IPython/core/events.py:89: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
            "  func(*args, **kwargs)\n",
            "/usr/local/lib/python3.10/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
            "  fig.canvas.print_figure(bytes_io, **kw)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x800 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "city_long_border = (-74.03, -73.75)\n",
        "city_lat_border = (40.63, 40.85)\n",
        "\n",
        "fig, axes = plt.subplots(nrows = 1, ncols = 2, figsize = (15, 8))\n",
        "\n",
        "scatter_pickup = sns.scatterplot(\n",
        "    data = taxi_data,\n",
        "    x = 'pickup_longitude',\n",
        "    y = 'pickup_latitude',\n",
        "    hue = 'geo_cluster',\n",
        "    s = 1,\n",
        "    ax = axes[0]\n",
        ")\n",
        "scatter_pickup.set_title('Координаты начала поездок')\n",
        "scatter_pickup.set_xlim(city_long_border)\n",
        "scatter_pickup.set_ylim(city_lat_border)\n",
        "\n",
        "scatter_dropoff = sns.scatterplot(\n",
        "    data = taxi_data,\n",
        "    x = 'dropoff_longitude',\n",
        "    y = 'dropoff_latitude',\n",
        "    hue = 'geo_cluster',\n",
        "    s = 1,\n",
        "    ax = axes[1]\n",
        ")\n",
        "scatter_dropoff.set_title('Координаты завершения поездок')\n",
        "scatter_dropoff.set_xlim(city_long_border)\n",
        "scatter_dropoff.set_ylim(city_lat_border);"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Ужасно долго строились диаграммы:)"
      ],
      "metadata": {
        "id": "KI5GvL8hb03g"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cSKZEkO4kBLq"
      },
      "source": [
        "## 4. Отбор и преобразование признаков"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Erg_Ejz1kBLr"
      },
      "source": [
        "Перед тем как перейти к построению модели, осталось сделать ещё несколько шагов.\n",
        "* Следует помнить, что многие алгоритмы машинного обучения не могут обрабатывать категориальные признаки в их обычном виде. Поэтому нам необходимо их закодировать;\n",
        "* Надо отобрать признаки, которые мы будем использовать для обучения модели;\n",
        "*  Необходимо масштабировать и трансформировать некоторые признаки для того, чтобы улучшить сходимость моделей, в основе которых лежат численные методы.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 122,
      "metadata": {
        "id": "N7MHv9HKkBLr",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "111afcd6-1805-4192-cc9d-37160203e8ae"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Shape of data: (1458233, 30)\n",
            "Columns: Index(['id', 'vendor_id', 'pickup_datetime', 'dropoff_datetime',\n",
            "       'passenger_count', 'pickup_longitude', 'pickup_latitude',\n",
            "       'dropoff_longitude', 'dropoff_latitude', 'store_and_fwd_flag',\n",
            "       'trip_duration', 'pickup_date', 'pickup_hour', 'pickup_day_of_week',\n",
            "       'day', 'date_x', 'holiday', 'pickup_holiday', 'total_distance',\n",
            "       'total_travel_time', 'number_of_steps', 'haversine_distance',\n",
            "       'direction', 'geo_cluster', 'temperature', 'visibility', 'wind speed',\n",
            "       'precip', 'events', 'trip_duration_log'],\n",
            "      dtype='object')\n"
          ]
        }
      ],
      "source": [
        "print('Shape of data: {}'.format(taxi_data.shape))\n",
        "print('Columns: {}'.format(taxi_data.columns))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "j6sgAOFqkBLr"
      },
      "source": [
        "Для удобства работы сделаем копию исходной таблицы с поездками:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 123,
      "metadata": {
        "id": "7rxobyQFkBLr",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 360
        },
        "outputId": "351404cf-58f2-48f8-a74d-375433d303e1"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          id vendor_id     pickup_datetime     dropoff_datetime  \\\n",
              "0  id2875421         2 2016-03-14 17:24:55  2016-03-14 17:32:30   \n",
              "1  id2377394         1 2016-06-12 00:43:35  2016-06-12 00:54:38   \n",
              "2  id3858529         2 2016-01-19 11:35:24  2016-01-19 12:10:48   \n",
              "3  id3504673         2 2016-04-06 19:32:31  2016-04-06 19:39:40   \n",
              "4  id2181028         2 2016-03-26 13:30:55  2016-03-26 13:38:10   \n",
              "\n",
              "   passenger_count  pickup_longitude  pickup_latitude  dropoff_longitude  \\\n",
              "0                1        -73.982155        40.767937         -73.964630   \n",
              "1                1        -73.980415        40.738564         -73.999481   \n",
              "2                1        -73.979027        40.763939         -74.005333   \n",
              "3                1        -74.010040        40.719971         -74.012268   \n",
              "4                1        -73.973053        40.793209         -73.972923   \n",
              "\n",
              "   dropoff_latitude store_and_fwd_flag  ...  number_of_steps  \\\n",
              "0         40.765602                  N  ...              5.0   \n",
              "1         40.731152                  N  ...              6.0   \n",
              "2         40.710087                  N  ...             16.0   \n",
              "3         40.706718                  N  ...              4.0   \n",
              "4         40.782520                  N  ...              5.0   \n",
              "\n",
              "  haversine_distance   direction  geo_cluster temperature visibility  \\\n",
              "0           1.498521   99.970196            9         4.4        8.0   \n",
              "1           1.805507 -117.153768            4        28.9       16.1   \n",
              "2           6.385098 -159.680165            4        -6.7       16.1   \n",
              "3           1.485498 -172.737700            4         7.2       16.1   \n",
              "4           1.188588  179.473585            9         9.4       16.1   \n",
              "\n",
              "  wind speed  precip  events  trip_duration_log  \n",
              "0       27.8     0.3    None           6.122493  \n",
              "1        7.4     0.0    None           6.498282  \n",
              "2       24.1     0.0    None           7.661527  \n",
              "3       25.9     0.0    None           6.063785  \n",
              "4        9.3     0.0    None           6.077642  \n",
              "\n",
              "[5 rows x 30 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-5d025ffe-c10a-4e78-bdc7-81214bc208c9\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>vendor_id</th>\n",
              "      <th>pickup_datetime</th>\n",
              "      <th>dropoff_datetime</th>\n",
              "      <th>passenger_count</th>\n",
              "      <th>pickup_longitude</th>\n",
              "      <th>pickup_latitude</th>\n",
              "      <th>dropoff_longitude</th>\n",
              "      <th>dropoff_latitude</th>\n",
              "      <th>store_and_fwd_flag</th>\n",
              "      <th>...</th>\n",
              "      <th>number_of_steps</th>\n",
              "      <th>haversine_distance</th>\n",
              "      <th>direction</th>\n",
              "      <th>geo_cluster</th>\n",
              "      <th>temperature</th>\n",
              "      <th>visibility</th>\n",
              "      <th>wind speed</th>\n",
              "      <th>precip</th>\n",
              "      <th>events</th>\n",
              "      <th>trip_duration_log</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>id2875421</td>\n",
              "      <td>2</td>\n",
              "      <td>2016-03-14 17:24:55</td>\n",
              "      <td>2016-03-14 17:32:30</td>\n",
              "      <td>1</td>\n",
              "      <td>-73.982155</td>\n",
              "      <td>40.767937</td>\n",
              "      <td>-73.964630</td>\n",
              "      <td>40.765602</td>\n",
              "      <td>N</td>\n",
              "      <td>...</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.498521</td>\n",
              "      <td>99.970196</td>\n",
              "      <td>9</td>\n",
              "      <td>4.4</td>\n",
              "      <td>8.0</td>\n",
              "      <td>27.8</td>\n",
              "      <td>0.3</td>\n",
              "      <td>None</td>\n",
              "      <td>6.122493</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>id2377394</td>\n",
              "      <td>1</td>\n",
              "      <td>2016-06-12 00:43:35</td>\n",
              "      <td>2016-06-12 00:54:38</td>\n",
              "      <td>1</td>\n",
              "      <td>-73.980415</td>\n",
              "      <td>40.738564</td>\n",
              "      <td>-73.999481</td>\n",
              "      <td>40.731152</td>\n",
              "      <td>N</td>\n",
              "      <td>...</td>\n",
              "      <td>6.0</td>\n",
              "      <td>1.805507</td>\n",
              "      <td>-117.153768</td>\n",
              "      <td>4</td>\n",
              "      <td>28.9</td>\n",
              "      <td>16.1</td>\n",
              "      <td>7.4</td>\n",
              "      <td>0.0</td>\n",
              "      <td>None</td>\n",
              "      <td>6.498282</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>id3858529</td>\n",
              "      <td>2</td>\n",
              "      <td>2016-01-19 11:35:24</td>\n",
              "      <td>2016-01-19 12:10:48</td>\n",
              "      <td>1</td>\n",
              "      <td>-73.979027</td>\n",
              "      <td>40.763939</td>\n",
              "      <td>-74.005333</td>\n",
              "      <td>40.710087</td>\n",
              "      <td>N</td>\n",
              "      <td>...</td>\n",
              "      <td>16.0</td>\n",
              "      <td>6.385098</td>\n",
              "      <td>-159.680165</td>\n",
              "      <td>4</td>\n",
              "      <td>-6.7</td>\n",
              "      <td>16.1</td>\n",
              "      <td>24.1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>None</td>\n",
              "      <td>7.661527</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>id3504673</td>\n",
              "      <td>2</td>\n",
              "      <td>2016-04-06 19:32:31</td>\n",
              "      <td>2016-04-06 19:39:40</td>\n",
              "      <td>1</td>\n",
              "      <td>-74.010040</td>\n",
              "      <td>40.719971</td>\n",
              "      <td>-74.012268</td>\n",
              "      <td>40.706718</td>\n",
              "      <td>N</td>\n",
              "      <td>...</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1.485498</td>\n",
              "      <td>-172.737700</td>\n",
              "      <td>4</td>\n",
              "      <td>7.2</td>\n",
              "      <td>16.1</td>\n",
              "      <td>25.9</td>\n",
              "      <td>0.0</td>\n",
              "      <td>None</td>\n",
              "      <td>6.063785</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>id2181028</td>\n",
              "      <td>2</td>\n",
              "      <td>2016-03-26 13:30:55</td>\n",
              "      <td>2016-03-26 13:38:10</td>\n",
              "      <td>1</td>\n",
              "      <td>-73.973053</td>\n",
              "      <td>40.793209</td>\n",
              "      <td>-73.972923</td>\n",
              "      <td>40.782520</td>\n",
              "      <td>N</td>\n",
              "      <td>...</td>\n",
              "      <td>5.0</td>\n",
              "      <td>1.188588</td>\n",
              "      <td>179.473585</td>\n",
              "      <td>9</td>\n",
              "      <td>9.4</td>\n",
              "      <td>16.1</td>\n",
              "      <td>9.3</td>\n",
              "      <td>0.0</td>\n",
              "      <td>None</td>\n",
              "      <td>6.077642</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 30 columns</p>\n",
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              "        document.querySelector('#df-5d025ffe-c10a-4e78-bdc7-81214bc208c9 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-5d025ffe-c10a-4e78-bdc7-81214bc208c9');\n",
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "\n",
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              "  .colab-df-quickchart {\n",
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              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
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              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
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              "\n",
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              "    border-bottom-color: var(--fill-color);\n",
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              "      spin 1s steps(1) infinite;\n",
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              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
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              "      border-left-color: var(--fill-color);\n",
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              "      border-left-color: var(--fill-color);\n",
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              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
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              "      border-bottom-color: var(--fill-color);\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
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              "        document.querySelector('#df-9ebe67ea-91c3-4993-b44e-99b70e1110bb button');\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "train_data"
            }
          },
          "metadata": {},
          "execution_count": 123
        }
      ],
      "source": [
        "train_data = taxi_data.copy()\n",
        "train_data.head()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_data.info()"
      ],
      "metadata": {
        "id": "kKYauH0zkZkM",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "aef81593-abd3-4043-a7ee-495236b3726e"
      },
      "execution_count": 124,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Index: 1458233 entries, 0 to 1458643\n",
            "Data columns (total 30 columns):\n",
            " #   Column              Non-Null Count    Dtype         \n",
            "---  ------              --------------    -----         \n",
            " 0   id                  1458233 non-null  object        \n",
            " 1   vendor_id           1458233 non-null  category      \n",
            " 2   pickup_datetime     1458233 non-null  datetime64[ns]\n",
            " 3   dropoff_datetime    1458233 non-null  object        \n",
            " 4   passenger_count     1458233 non-null  int64         \n",
            " 5   pickup_longitude    1458233 non-null  float64       \n",
            " 6   pickup_latitude     1458233 non-null  float64       \n",
            " 7   dropoff_longitude   1458233 non-null  float64       \n",
            " 8   dropoff_latitude    1458233 non-null  float64       \n",
            " 9   store_and_fwd_flag  1458233 non-null  object        \n",
            " 10  trip_duration       1458233 non-null  int64         \n",
            " 11  pickup_date         1458233 non-null  object        \n",
            " 12  pickup_hour         1458233 non-null  int32         \n",
            " 13  pickup_day_of_week  1458233 non-null  int32         \n",
            " 14  day                 51101 non-null    object        \n",
            " 15  date_x              51101 non-null    object        \n",
            " 16  holiday             51101 non-null    object        \n",
            " 17  pickup_holiday      1458233 non-null  int64         \n",
            " 18  total_distance      1458233 non-null  float64       \n",
            " 19  total_travel_time   1458233 non-null  float64       \n",
            " 20  number_of_steps     1458233 non-null  float64       \n",
            " 21  haversine_distance  1458233 non-null  float64       \n",
            " 22  direction           1458233 non-null  float64       \n",
            " 23  geo_cluster         1458233 non-null  int32         \n",
            " 24  temperature         1458233 non-null  float64       \n",
            " 25  visibility          1458233 non-null  float64       \n",
            " 26  wind speed          1458233 non-null  float64       \n",
            " 27  precip              1458233 non-null  float64       \n",
            " 28  events              1458233 non-null  object        \n",
            " 29  trip_duration_log   1458233 non-null  float64       \n",
            "dtypes: category(1), datetime64[ns](1), float64(14), int32(3), int64(3), object(8)\n",
            "memory usage: 318.5+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U7i7DFBdkBLr"
      },
      "source": [
        "### Задание 4.1.\n",
        "Сразу позаботимся об очевидных неинформативных и избыточных признаках.\n",
        "\n",
        "а) Какой из признаков является уникальным для каждой поездки и не несет полезной информации в определении ее продолжительности?\n",
        "\n",
        "б) Утечка данных (data leak) - это…\n",
        "\n",
        "в) Подумайте, наличие какого из признаков в обучающем наборе данных создает утечку данных?\n",
        "\n",
        "г) Исключите выбранные в пунктах а) и в) признаки из исходной таблицы с данными. Сколько столбцов в таблице у вас осталось?\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**а)** Нам не нужны id\n",
        "\n",
        "**б)** Утечка данных - это когда данные, используемые в процессе обучения, содержат информацию о том, что модель пытается предсказать\n",
        "\n",
        "**в)** Время окончания поездки не несет никакой смысловой нагрузки, т.к. наша задача - предсказать продолжительность, а, значит, этот признак фактически приведет к утечке данных\n",
        "**г)** Мы так же исключим лишние столбцы, оставшиеся от объединения таблицы поездок с таблицей праздников - day, date_x, holiday"
      ],
      "metadata": {
        "id": "EXHlJ8SEjZjI"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 125,
      "metadata": {
        "id": "-hg53Bv1kBLr"
      },
      "outputs": [],
      "source": [
        "# ваш код здесь\n",
        "train_data = train_data.drop(['id', 'dropoff_datetime', 'day', 'date_x', 'holiday'], axis = 1)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_data.info() #оставили 25 колонок"
      ],
      "metadata": {
        "id": "HfVj11EpliMx",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "99dbc904-b7ed-4c02-ed9e-8702b892cc23"
      },
      "execution_count": 126,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Index: 1458233 entries, 0 to 1458643\n",
            "Data columns (total 25 columns):\n",
            " #   Column              Non-Null Count    Dtype         \n",
            "---  ------              --------------    -----         \n",
            " 0   vendor_id           1458233 non-null  category      \n",
            " 1   pickup_datetime     1458233 non-null  datetime64[ns]\n",
            " 2   passenger_count     1458233 non-null  int64         \n",
            " 3   pickup_longitude    1458233 non-null  float64       \n",
            " 4   pickup_latitude     1458233 non-null  float64       \n",
            " 5   dropoff_longitude   1458233 non-null  float64       \n",
            " 6   dropoff_latitude    1458233 non-null  float64       \n",
            " 7   store_and_fwd_flag  1458233 non-null  object        \n",
            " 8   trip_duration       1458233 non-null  int64         \n",
            " 9   pickup_date         1458233 non-null  object        \n",
            " 10  pickup_hour         1458233 non-null  int32         \n",
            " 11  pickup_day_of_week  1458233 non-null  int32         \n",
            " 12  pickup_holiday      1458233 non-null  int64         \n",
            " 13  total_distance      1458233 non-null  float64       \n",
            " 14  total_travel_time   1458233 non-null  float64       \n",
            " 15  number_of_steps     1458233 non-null  float64       \n",
            " 16  haversine_distance  1458233 non-null  float64       \n",
            " 17  direction           1458233 non-null  float64       \n",
            " 18  geo_cluster         1458233 non-null  int32         \n",
            " 19  temperature         1458233 non-null  float64       \n",
            " 20  visibility          1458233 non-null  float64       \n",
            " 21  wind speed          1458233 non-null  float64       \n",
            " 22  precip              1458233 non-null  float64       \n",
            " 23  events              1458233 non-null  object        \n",
            " 24  trip_duration_log   1458233 non-null  float64       \n",
            "dtypes: category(1), datetime64[ns](1), float64(14), int32(3), int64(3), object(3)\n",
            "memory usage: 262.8+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NHyYd7XzkBLr"
      },
      "source": [
        "Ранее мы извлекли всю необходимую для нас информацию из даты начала поездки, теперь мы можем избавиться от этих признаков, так как они нам больше не понадобятся:\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 127,
      "metadata": {
        "id": "nzO9ioMHkBLs",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7d8b4633-400f-440c-e1fa-d3b71b1aa887"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Shape of data:  (1458233, 23)\n"
          ]
        }
      ],
      "source": [
        "drop_columns = ['pickup_datetime', 'pickup_date']\n",
        "train_data = train_data.drop(drop_columns, axis=1)\n",
        "print('Shape of data:  {}'.format(train_data.shape))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cXHbkc3ykBLs"
      },
      "source": [
        "### Задание 4.2.\n",
        "\n",
        "Закодируйте признак vendor_id в таблице train_data таким образом, чтобы он был равен 0, если идентификатор таксопарка равен 1, и 1 — в противном случае.\n",
        "\n",
        "Закодируйте признак store_and_fwd_flag в таблице train_data таким образом, чтобы он был равен 0, если флаг выставлен в значение 'N', и 1 — в противном случае.\n",
        "\n",
        "а) Рассчитайте среднее по закодированному столбцу vendor_id. Ответ приведите с точностью до сотых.\n",
        "\n",
        "б) Рассчитайте среднее по закодированному столбцу store_and_fwd_flag. Ответ приведите с точностью до тысячных.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 128,
      "metadata": {
        "id": "_O1jJaZtkBLs",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 461
        },
        "outputId": "93c0e671-7699-45ed-91e7-b4975f3058ea"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         vendor_id  passenger_count  pickup_longitude  pickup_latitude  \\\n",
              "0                1                1        -73.982155        40.767937   \n",
              "1                0                1        -73.980415        40.738564   \n",
              "2                1                1        -73.979027        40.763939   \n",
              "3                1                1        -74.010040        40.719971   \n",
              "4                1                1        -73.973053        40.793209   \n",
              "...            ...              ...               ...              ...   \n",
              "1458639          1                4        -73.982201        40.745522   \n",
              "1458640          0                1        -74.000946        40.747379   \n",
              "1458641          1                1        -73.959129        40.768799   \n",
              "1458642          0                1        -73.982079        40.749062   \n",
              "1458643          0                1        -73.979538        40.781750   \n",
              "\n",
              "         dropoff_longitude  dropoff_latitude  store_and_fwd_flag  \\\n",
              "0               -73.964630         40.765602                   0   \n",
              "1               -73.999481         40.731152                   0   \n",
              "2               -74.005333         40.710087                   0   \n",
              "3               -74.012268         40.706718                   0   \n",
              "4               -73.972923         40.782520                   0   \n",
              "...                    ...               ...                 ...   \n",
              "1458639         -73.994911         40.740170                   0   \n",
              "1458640         -73.970184         40.796547                   0   \n",
              "1458641         -74.004433         40.707371                   0   \n",
              "1458642         -73.974632         40.757107                   0   \n",
              "1458643         -73.972809         40.790585                   0   \n",
              "\n",
              "         trip_duration  pickup_hour  pickup_day_of_week  ...  number_of_steps  \\\n",
              "0                  455           17                   0  ...              5.0   \n",
              "1                  663            0                   6  ...              6.0   \n",
              "2                 2124           11                   1  ...             16.0   \n",
              "3                  429           19                   2  ...              4.0   \n",
              "4                  435           13                   5  ...              5.0   \n",
              "...                ...          ...                 ...  ...              ...   \n",
              "1458639            778           13                   4  ...              8.0   \n",
              "1458640            655            7                   6  ...             11.0   \n",
              "1458641            764            6                   4  ...             10.0   \n",
              "1458642            373           15                   1  ...              7.0   \n",
              "1458643            198           14                   1  ...              2.0   \n",
              "\n",
              "         haversine_distance   direction  geo_cluster  temperature  visibility  \\\n",
              "0                  1.498521   99.970196            9          4.4         8.0   \n",
              "1                  1.805507 -117.153768            4         28.9        16.1   \n",
              "2                  6.385098 -159.680165            4         -6.7        16.1   \n",
              "3                  1.485498 -172.737700            4          7.2        16.1   \n",
              "4                  1.188588  179.473585            9          9.4        16.1   \n",
              "...                     ...         ...          ...          ...         ...   \n",
              "1458639            1.225080 -119.059338            0          7.8        16.1   \n",
              "1458640            6.049836   25.342196            9          7.2         2.8   \n",
              "1458641            7.824606 -150.788492            4         18.3        16.1   \n",
              "1458642            1.092564   35.033294            0         -2.8        16.1   \n",
              "1458643            1.134042   29.969486            9          2.2        16.1   \n",
              "\n",
              "         wind speed  precip  events  trip_duration_log  \n",
              "0              27.8     0.3    None           6.122493  \n",
              "1               7.4     0.0    None           6.498282  \n",
              "2              24.1     0.0    None           7.661527  \n",
              "3              25.9     0.0    None           6.063785  \n",
              "4               9.3     0.0    None           6.077642  \n",
              "...             ...     ...     ...                ...  \n",
              "1458639        11.1     0.0    None           6.658011  \n",
              "1458640        18.5     8.1    Rain           6.486161  \n",
              "1458641         0.0     0.0    None           6.639876  \n",
              "1458642         9.3     0.0    None           5.924256  \n",
              "1458643        16.7     0.0    None           5.293305  \n",
              "\n",
              "[1458233 rows x 23 columns]"
            ],
            "text/html": [
              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>vendor_id</th>\n",
              "      <th>passenger_count</th>\n",
              "      <th>pickup_longitude</th>\n",
              "      <th>pickup_latitude</th>\n",
              "      <th>dropoff_longitude</th>\n",
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              "      <td>99.970196</td>\n",
              "      <td>9</td>\n",
              "      <td>4.4</td>\n",
              "      <td>8.0</td>\n",
              "      <td>27.8</td>\n",
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              "      <td>6.122493</td>\n",
              "    </tr>\n",
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              "      <td>40.710087</td>\n",
              "      <td>0</td>\n",
              "      <td>2124</td>\n",
              "      <td>11</td>\n",
              "      <td>1</td>\n",
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              "      <td>16.0</td>\n",
              "      <td>6.385098</td>\n",
              "      <td>-159.680165</td>\n",
              "      <td>4</td>\n",
              "      <td>-6.7</td>\n",
              "      <td>16.1</td>\n",
              "      <td>24.1</td>\n",
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              "      <td>7.661527</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
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              "      <td>-74.012268</td>\n",
              "      <td>40.706718</td>\n",
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              "      <td>429</td>\n",
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              "      <td>-172.737700</td>\n",
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              "      <td>7.2</td>\n",
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              "      <td>6.063785</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
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              "      <td>435</td>\n",
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              "      <td>6.658011</td>\n",
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              "      <td>9.3</td>\n",
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              "      <td>5.924256</td>\n",
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              "    <tr>\n",
              "      <th>1458643</th>\n",
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              "      <td>16.7</td>\n",
              "      <td>0.0</td>\n",
              "      <td>None</td>\n",
              "      <td>5.293305</td>\n",
              "    </tr>\n",
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              "</table>\n",
              "<p>1458233 rows × 23 columns</p>\n",
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              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "\n",
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              "\n",
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              "        document.querySelector('#df-8d2fdd4c-89fe-4dab-a49a-9e46aa54939b button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-8d2fdd4c-89fe-4dab-a49a-9e46aa54939b');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "        docLink.innerHTML = docLinkHtml;\n",
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              "</svg>\n",
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              "\n",
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              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
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              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
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              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
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              "      border-right-color: var(--fill-color);\n",
              "    }\n",
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              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-4e2085fd-f685-4d5f-a3fd-1b7b3853cd6d button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "train_data"
            }
          },
          "metadata": {},
          "execution_count": 128
        }
      ],
      "source": [
        "# ваш код здесь\n",
        "train_data['vendor_id'] = train_data['vendor_id'].map(lambda x: 0 if x == 1 else 1).astype(int)\n",
        "train_data['store_and_fwd_flag'] = train_data['store_and_fwd_flag'].map(lambda x: 0 if x == 'N' else 1)\n",
        "train_data"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#среднее по vendor_id\n",
        "round(train_data['vendor_id'].mean(), 2)"
      ],
      "metadata": {
        "id": "gFqeyWgKmZ_1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9be29b85-7826-4a7b-bb43-dd98a91c3d69"
      },
      "execution_count": 129,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.53"
            ]
          },
          "metadata": {},
          "execution_count": 129
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "round(train_data['store_and_fwd_flag'].mean(), 2)"
      ],
      "metadata": {
        "id": "fuSgLMSEmtxb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ff902283-f090-4eb4-a88e-1c61dfc006e9"
      },
      "execution_count": 130,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.01"
            ]
          },
          "metadata": {},
          "execution_count": 130
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XMUmtLu1kBLs"
      },
      "source": [
        "### Задание 4.3.\n",
        "Создайте таблицу data_onehot из закодированных однократным кодированием признаков pickup_day_of_week, geo_cluster и events в таблице train_data с помощью OneHotEncoder из библиотеки sklearn. Параметр drop выставите в значение 'first', чтобы удалять первый бинарный столбец, тем самым не создавая излишних признаков.\n",
        "\n",
        "В параметре handle_unknown установите значение 'ignore'. Это позволит коду отработать без ошибок в случае появления в тестовой выборке значений, отсутствующих в обучающей выборке.\n",
        "\n",
        "В результате работы OneHotEncoder вы получите безымянный numpy-массив, который нам будет необходимо преобразовать обратно в DataFrame, для более удобной работы в дальнейшем. Чтобы получить имена закодированных столбцов у объекта типа OneHotEncoder есть специальный метод get_feature_names_out(). Он возвращает список новых закодированных имен столбцов в формате <оригинальное имя столбца>_<имя категории>.\n",
        "\n",
        "Пример использования:\n",
        "\n",
        "``` python\n",
        "# Объявляем кодировщик\n",
        "one_hot_encoder = OneHotEncoder(drop='first', handle_unknown='ignore')\n",
        "# Получаем закодированные имена столбцов\n",
        "column_names = one_hot_encoder.get_feature_names_out()\n",
        "# Составляем DataFrame из закодированных признаков\n",
        "data_onehot = pd.DataFrame(data_onehot, columns=column_names)\n",
        "```\n",
        "\n",
        "В этом псевдокоде:\n",
        "* one_hot_encoder - объект класса OneHotEncoder\n",
        "* data_onehot - numpy-массив, полученный в результате трансформации кодировщиком\n",
        "\n",
        "В результате выполнения задания у вас должен быть образован DataFrame `data_onehot`, который содержит кодированные категориальные признаки pickup_day_of_week, geo_cluster и events.\n",
        "\n",
        "\n",
        "Сколько бинарных столбцов у вас получилось сгенерировать с помощью однократного кодирования?\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 131,
      "metadata": {
        "id": "BonLFJbhkBLs",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "004206e0-e091-49a5-b495-fb3da9e55d72"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1458233 entries, 0 to 1458232\n",
            "Data columns (total 18 columns):\n",
            " #   Column                Non-Null Count    Dtype  \n",
            "---  ------                --------------    -----  \n",
            " 0   pickup_day_of_week_1  1458233 non-null  float64\n",
            " 1   pickup_day_of_week_2  1458233 non-null  float64\n",
            " 2   pickup_day_of_week_3  1458233 non-null  float64\n",
            " 3   pickup_day_of_week_4  1458233 non-null  float64\n",
            " 4   pickup_day_of_week_5  1458233 non-null  float64\n",
            " 5   pickup_day_of_week_6  1458233 non-null  float64\n",
            " 6   geo_cluster_1         1458233 non-null  float64\n",
            " 7   geo_cluster_2         1458233 non-null  float64\n",
            " 8   geo_cluster_3         1458233 non-null  float64\n",
            " 9   geo_cluster_4         1458233 non-null  float64\n",
            " 10  geo_cluster_5         1458233 non-null  float64\n",
            " 11  geo_cluster_6         1458233 non-null  float64\n",
            " 12  geo_cluster_7         1458233 non-null  float64\n",
            " 13  geo_cluster_8         1458233 non-null  float64\n",
            " 14  geo_cluster_9         1458233 non-null  float64\n",
            " 15  events_None           1458233 non-null  float64\n",
            " 16  events_Rain           1458233 non-null  float64\n",
            " 17  events_Snow           1458233 non-null  float64\n",
            "dtypes: float64(18)\n",
            "memory usage: 200.3 MB\n"
          ]
        }
      ],
      "source": [
        "# ваш код здесь\n",
        "one_hot_encoder = preprocessing.OneHotEncoder(drop = 'first')\n",
        "columns_to_change = ['pickup_day_of_week', 'geo_cluster', 'events']\n",
        "data_onehot = one_hot_encoder.fit_transform(\n",
        "    train_data[columns_to_change]).toarray()\n",
        "\n",
        "column_names = one_hot_encoder.get_feature_names_out()\n",
        "\n",
        "data_onehot = pd.DataFrame(data_onehot, columns = column_names)\n",
        "\n",
        "data_onehot.info() #18 столбцов"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AsGsYqHRkBLs"
      },
      "source": [
        "Добавим полученную таблицу с закодированными признаками:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 132,
      "metadata": {
        "id": "blXeWiS0kBLs",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b562da08-5043-407b-d520-6240a45f70bd"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Shape of data: (1458233, 38)\n"
          ]
        }
      ],
      "source": [
        "train_data = pd.concat(\n",
        "    [train_data.reset_index(drop=True).drop(columns_to_change, axis=1), data_onehot],\n",
        "    axis=1\n",
        ")\n",
        "print('Shape of data: {}'.format(train_data.shape))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XQhXAud5kBLt"
      },
      "source": [
        "Теперь, когда категориальные признаки предобработаны, сформируем матрицу наблюдений X, вектор целевой переменной y и его логарифм y_log. В матрицу наблюдений войдут все столбцы из таблицы с поездками за исключением целевого признака trip_duration и его логарифмированной версии trip_duration_log:\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 133,
      "metadata": {
        "id": "Vz6YfcALkBLt"
      },
      "outputs": [],
      "source": [
        "X = train_data.drop(['trip_duration', 'trip_duration_log'], axis=1)\n",
        "y = train_data['trip_duration']\n",
        "y_log = train_data['trip_duration_log']"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "orPjdejMkBLt"
      },
      "source": [
        "Все наши модели мы будем обучать на логарифмированной версии y_log."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "L8CzG9CnkBLt"
      },
      "source": [
        "Выбранный тип валидации - hold-out. Разобьем выборку на обучающую и валидационную в соотношении 67/33:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 134,
      "metadata": {
        "id": "hPEJ89ZdkBLt"
      },
      "outputs": [],
      "source": [
        "X_train, X_valid, y_train_log, y_valid_log = model_selection.train_test_split(\n",
        "    X, y_log,\n",
        "    test_size=0.33,\n",
        "    random_state=42\n",
        ")\n",
        "# OOT = out of time"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yL9ZPlW3kBLt"
      },
      "source": [
        "На данный момент у нас достаточно много признаков: скорее всего, не все из них будут важны. Давайте оставим лишь те, которые сильнее всего связаны с целевой переменной и точно будут вносить вклад в повышение качества модели.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AaqdJUp-kBLt"
      },
      "source": [
        "### Задание 4.4.\n",
        "С помощью SelectKBest отберите 25 признаков, наилучшим образом подходящих для предсказания целевой переменной в логарифмическом масштабе. Отбор реализуйте по обучающей выборке, используя параметр score_func = f_regression.\n",
        "\n",
        "Укажите признаки, которые вошли в список отобранных\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 135,
      "metadata": {
        "id": "TEeqoG20kBLt",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "163f5353-39b5-449f-bbfb-d7a445b91c70"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['vendor_id', 'passenger_count', 'pickup_longitude',\n",
              "       'pickup_latitude', 'dropoff_longitude', 'dropoff_latitude',\n",
              "       'store_and_fwd_flag', 'pickup_hour', 'pickup_holiday',\n",
              "       'total_distance', 'total_travel_time', 'number_of_steps',\n",
              "       'haversine_distance', 'temperature', 'pickup_day_of_week_1',\n",
              "       'pickup_day_of_week_2', 'pickup_day_of_week_3',\n",
              "       'pickup_day_of_week_4', 'pickup_day_of_week_5',\n",
              "       'pickup_day_of_week_6', 'geo_cluster_1', 'geo_cluster_3',\n",
              "       'geo_cluster_5', 'geo_cluster_7', 'geo_cluster_9'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 135
        }
      ],
      "source": [
        "# ваш код здесь\n",
        "selector = feature_selection.SelectKBest(\n",
        "    score_func = feature_selection.f_regression,\n",
        "    k = 25\n",
        ")\n",
        "selector.fit(X_train, y_train_log)\n",
        "\n",
        "features = selector.get_feature_names_out()\n",
        "X_train = X_train[features]\n",
        "X_valid = X_valid[features]\n",
        "features"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JLOJ_kdlkBLt"
      },
      "source": [
        "Так как мы будем использовать различные модели, в том числе внутри которых заложены численные методы оптимизации, то давайте заранее позаботимся о масштабировании факторов.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9WfHLiGKkBLu"
      },
      "source": [
        "### Задание 4.5.\n",
        "Нормализуйте предикторы в обучающей и валидационной выборках с помощью MinMaxScaler из библиотеки sklearn. Помните, что обучение нормализатора производится на обучающей выборке, а трансформация на обучающей и валидационной!\n",
        "\n",
        "Рассчитайте среднее арифметическое для первого предиктора (т. е. для первого столбца матрицы) из валидационной выборки. Ответ округлите до сотых.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 136,
      "metadata": {
        "id": "WSslyUgwkBLu",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fbd3669c-44af-487d-b034-1c8d25aaa75e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.54"
            ]
          },
          "metadata": {},
          "execution_count": 136
        }
      ],
      "source": [
        "# ваш код здесь\n",
        "mm_scaler = preprocessing.MinMaxScaler()\n",
        "\n",
        "X_train = mm_scaler.fit_transform(X_train)\n",
        "X_valid = mm_scaler.transform(X_valid)\n",
        "\n",
        "np.round(np.mean(X_valid[:, 0]), 2)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gaEMuA6skBLu"
      },
      "source": [
        "## 5. Решение задачи регрессии: линейная регрессия и деревья решений"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "y-pS_2XykBLu"
      },
      "source": [
        "Определим метрику, по которой мы будем измерять качество наших моделей. Мы будем следовать канонам исходного соревнования на Kaggle и в качестве метрики использовать RMSLE (Root Mean Squared Log Error), которая вычисляется как:\n",
        "$$RMSLE = \\sqrt{\\frac{1}{n}\\sum_{i=1}^n(log(y_i+1)-log(\\hat{y_i}+1))^2},$$\n",
        "где:\n",
        "* $y_i$ - истинная длительность i-ой поездки на такси (trip_duration)\n",
        "* $\\hat{y_i}$- предсказанная моделью длительность i-ой поездки на такси\n",
        "\n",
        "Заметим, что логарифмирование целевого признака мы уже провели заранее, поэтому нам будет достаточно вычислить метрику RMSE для модели, обученной прогнозировать длительность поездки такси в логарифмическом масштабе:\n",
        "$$z_i=log(y_i+1),$$\n",
        "$$RMSLE = \\sqrt{\\frac{1}{n}\\sum_{i=1}^n(z_i-\\hat{z_i})^2}=\\sqrt{MSE(z_i,\\hat{z_i})}$$\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6Qd3xY1-kBLu"
      },
      "source": [
        "### Задание 5.1.\n",
        "Постройте модель линейной регрессии на обучающей выборке (факторы должны быть нормализованы, целевую переменную используйте в логарифмическом масштабе). Все параметры оставьте по умолчанию.\n",
        "\n",
        "Для полученной модели рассчитайте метрику RMSLE на тренировочной и валидационной выборках. Ответ округлите до сотых.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 137,
      "metadata": {
        "id": "R1IB1yK5kBLu"
      },
      "outputs": [],
      "source": [
        "# ваш код здесь\n",
        "lr_model = linear_model.LinearRegression()\n",
        "\n",
        "lr_model.fit(X_train, y_train_log)\n",
        "\n",
        "y_train_pred = lr_model.predict(X_train)\n",
        "y_valid_pred = lr_model.predict(X_valid)\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#RMSLE тренировочная выборка\n",
        "round(np.sqrt(metrics.mean_squared_error(y_train_log, y_train_pred)), 2)"
      ],
      "metadata": {
        "id": "6mwDNUZqpIJR",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a524ca43-8242-43fb-a3d8-426f36a0496b"
      },
      "execution_count": 138,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.54"
            ]
          },
          "metadata": {},
          "execution_count": 138
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#RMSLE тестовая выборка\n",
        "round(np.sqrt(metrics.mean_squared_error(y_valid_log, y_valid_pred)), 2)"
      ],
      "metadata": {
        "id": "Th_mZH-apb69",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "07f55497-251f-4ff6-b1a6-1aff160b8904"
      },
      "execution_count": 139,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.54"
            ]
          },
          "metadata": {},
          "execution_count": 139
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QSN6i928kBLu"
      },
      "source": [
        "### Задание 5.2.\n",
        "Сгенерируйте полиномиальные признаки 2-ой степени с помощью PolynomialFeatures из библиотеки sklearn. Параметр include_bias выставите в значение False.\n",
        "\n",
        "Постройте модель полиномиальной регрессии 2-ой степени на обучающей выборке (факторы должны быть нормализованы, целевую переменную используйте в логарифмическом масштабе). Все параметры оставьте по умолчанию.\n",
        "\n",
        "а) Для полученной модели рассчитайте метрику RMSLE на тренировочной и валидационной выборках. Ответ округлите до сотых.\n",
        "\n",
        "б) Наблюдаются ли у вашей модели признаки переобучения?\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 140,
      "metadata": {
        "id": "gIIWA_arkBLu"
      },
      "outputs": [],
      "source": [
        "# ваш код здесь\n",
        "poly = preprocessing.PolynomialFeatures(degree = 2, include_bias = False)\n",
        "poly.fit(X_train)\n",
        "\n",
        "X_train_poly = poly.transform(X_train)\n",
        "X_valid_poly = poly.transform(X_valid)\n",
        "\n",
        "lr_poly = linear_model.LinearRegression()\n",
        "lr_poly.fit(X_train_poly, y_train_log)\n",
        "y_train_pred = lr_poly.predict(X_train_poly)\n",
        "y_valid_pred = lr_poly.predict(X_valid_poly)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# RMSLE тренировочная выборка\n",
        "round(np.sqrt(metrics.mean_squared_error(y_train_log, y_train_pred)), 2)"
      ],
      "metadata": {
        "id": "zvAwwY4EqnFg",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "86be9016-f933-4334-e409-34162fb0cca3"
      },
      "execution_count": 141,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.47"
            ]
          },
          "metadata": {},
          "execution_count": 141
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# RMSLE тестовая выборка\n",
        "round(np.sqrt(metrics.mean_squared_error(y_valid_log, y_valid_pred)), 2)"
      ],
      "metadata": {
        "id": "rkEecuE-qn48",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2a4be965-c17b-41cd-8f9e-9921a0afcc28"
      },
      "execution_count": 142,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7"
            ]
          },
          "metadata": {},
          "execution_count": 142
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Да, есть признаки переобучения\n"
      ],
      "metadata": {
        "id": "O08CwLY1xv71"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7ChD_MF6kBLv"
      },
      "source": [
        "### Задание 5.3.\n",
        "Постройте модель полиномиальной регрессии 2-ой степени с L2-регуляризацией (регуляризация по Тихонову) на обучающей выборке  (факторы должны быть нормализованы, целевую переменную используйте в логарифмическом масштабе). Коэффициент регуляризации $\\alpha$ установите равным 1, остальные параметры оставьте по умолчанию.\n",
        "\n",
        "Для полученной модели рассчитайте метрику RMSLE на тренировочной и валидационной выборках. Ответ округлите до сотых.\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "lr_ridge = linear_model.Ridge(alpha = 1)\n",
        "\n",
        "# обучаем модель\n",
        "lr_ridge.fit(X_train_poly, y_train_log)\n",
        "\n",
        "# делаем предсказания\n",
        "y_train_pred = lr_ridge.predict(X_train_poly)\n",
        "y_valid_pred = lr_ridge.predict(X_valid_poly)"
      ],
      "metadata": {
        "id": "_aeJzqCHq42M"
      },
      "execution_count": 143,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# RMSLE тренировочная выборка\n",
        "round(np.sqrt(metrics.mean_squared_error(y_train_log, y_train_pred)), 2)"
      ],
      "metadata": {
        "id": "YsExzDJSq_mP",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e03c23e5-03a8-4abb-8f5b-087084dca647"
      },
      "execution_count": 144,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.48"
            ]
          },
          "metadata": {},
          "execution_count": 144
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# RMSLE тренировочная выборка\n",
        "round(np.sqrt(metrics.mean_squared_error(y_valid_log, y_valid_pred)), 2)"
      ],
      "metadata": {
        "id": "1c-Dd9UXrFYn",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8c4f190a-868c-4a96-c45e-d750ef12e7f2"
      },
      "execution_count": 145,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.48"
            ]
          },
          "metadata": {},
          "execution_count": 145
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aU5EJsIqkBLv"
      },
      "source": [
        "### Задание 5.4.\n",
        "Постройте модель дерева решений (DecisionTreeRegressor) на обучающей выборке (факторы должны быть нормализованы, целевую переменную используйте в логарифмическом масштабе). Все параметры оставьте по умолчанию.\n",
        "\n",
        "а) Для полученной модели рассчитайте метрику RMSLE на тренировочной и валидационной выборках. Ответ округлите до сотых.\n",
        "\n",
        "б) Наблюдаются ли у вашей модели признаки переобучения?\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 146,
      "metadata": {
        "id": "SY1G4YcrkBLv"
      },
      "outputs": [],
      "source": [
        "# ваш код здесь\n",
        "dt_reg = tree.DecisionTreeRegressor(random_state = 42)\n",
        "dt_reg.fit(X_train, y_train_log)\n",
        "\n",
        "# делаем предсказания\n",
        "y_train_pred = dt_reg.predict(X_train)\n",
        "y_valid_pred = dt_reg.predict(X_valid)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# RMSLE тренировочная выборка\n",
        "round(np.sqrt(metrics.mean_squared_error(y_train_log, y_train_pred)), 2)"
      ],
      "metadata": {
        "id": "zEEpiaZ9rhdC",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f56c256f-bab5-4c54-feda-86b40222f73a"
      },
      "execution_count": 147,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0"
            ]
          },
          "metadata": {},
          "execution_count": 147
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# RMSLE тренировочная выборка\n",
        "round(np.sqrt(metrics.mean_squared_error(y_valid_log, y_valid_pred)), 2)"
      ],
      "metadata": {
        "id": "NuNTducdriQr",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cdd13217-c3bf-430e-9c17-1a8ac8c06c0c"
      },
      "execution_count": 148,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.57"
            ]
          },
          "metadata": {},
          "execution_count": 148
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Признаки переобучения наблюдаются"
      ],
      "metadata": {
        "id": "XuTFUBEPxT3K"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1YJIlj4ykBLv"
      },
      "source": [
        "### Задание 5.5.\n",
        "Переберите все возможные варианты глубины дерева решений в диапазоне от 7 до 20:\n",
        "\n",
        "max_depths = range(7, 20)\n",
        "\n",
        "Параметр random_state задайте равным 42.\n",
        "\n",
        "Постройте линейные графики изменения метрики RMSE на тренировочной и валидационной выборках в зависимости от значения параметра глубины дерева решений.\n",
        "\n",
        "а) Найдите оптимальное значение максимальной глубины дерева, для которой будет наблюдаться минимальное значение RMSLE на обучающей выборке, но при этом еще не будет наблюдаться переобучение (валидационная кривая еще не начинает возрастать).\n",
        "\n",
        "б) Чему равно значение метрик RMSLE на тренировочной и валидационной выборках для дерева решений с выбранной оптимальной глубиной? Ответ округлите до сотых.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 149,
      "metadata": {
        "id": "_MbTVYsWkBLv",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 440
        },
        "outputId": "0fac54b0-86c5-4717-e951-399f02bed6e6"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Оптимальная глубина дерева решений: 12\n",
            "RMSLE на тренировочной выборке: 0.41\n",
            "RMSLE на валидационной выборке: 0.43\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "max_depths = range(7, 20)\n",
        "\n",
        "train_scores = []\n",
        "valid_scores = []\n",
        "\n",
        "for depth in max_depths:\n",
        "    #DecisionTreeRegressor\n",
        "    dt_reg = tree.DecisionTreeRegressor(max_depth = depth, random_state = 42)\n",
        "\n",
        "    dt_reg.fit(X_train, y_train_log)\n",
        "    y_train_pred = dt_reg.predict(X_train)\n",
        "    y_valid_pred = dt_reg.predict(X_valid)\n",
        "    train_scores.append(\n",
        "        np.sqrt(metrics.mean_squared_error(y_train_log, y_train_pred))\n",
        "    )\n",
        "    valid_scores.append(\n",
        "        np.sqrt(metrics.mean_squared_error(y_valid_log, y_valid_pred))\n",
        "    )\n",
        "\n",
        "# визуализируем изменения RMSLE в зависимости от max_depth\n",
        "fig, ax = plt.subplots(figsize = (12, 4))          # фигура + координатная плоскость\n",
        "ax.plot(max_depths, train_scores, label = 'Train') # линейный график для тренировочной выборки\n",
        "ax.plot(max_depths, valid_scores, label = 'Valid') # линейный график для валидационной выборки\n",
        "ax.set_xlabel('max_depth')                         # название оси абсцисс\n",
        "ax.set_ylabel('RMSLE')                             # название оси ординат\n",
        "ax.set_xticks(max_depths)                          # метки на оси абсцисс\n",
        "ax.legend();                                       # отображение легенды\n",
        "\n",
        "# извлекаем индекс лучшего RMSLE на валидационной выборке\n",
        "best_index = valid_scores.index(min(valid_scores))\n",
        "print('Оптимальная глубина дерева решений:', max_depths[best_index])\n",
        "print('RMSLE на тренировочной выборке:', round(train_scores[best_index], 2))\n",
        "print('RMSLE на валидационной выборке:', round(valid_scores[best_index], 2))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zUXrz7YDkBLv"
      },
      "source": [
        "## 6. Решение задачи регрессии: ансамблевые методы и построение прогноза"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "297rnbaikBLv"
      },
      "source": [
        "Переходим к тяжелой артиллерии: ансамблевым алгоритмам."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZIAGlVjqkBLw"
      },
      "source": [
        "### Задание 6.1.\n",
        "\n",
        "Постройте модель случайного леса на обучающей выборке (факторы должны быть нормализованы, целевую переменную используйте в логарифмическом масштабе). В качестве гиперпараметров укажите следующие:\n",
        "* n_estimators=200,\n",
        "* max_depth=12,\n",
        "* criterion='squared_error',\n",
        "* min_samples_split=20,\n",
        "* random_state=42\n",
        "\n",
        "Для полученной модели рассчитайте метрику RMSLE на тренировочной и валидационной выборках. Ответ округлите до сотых.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 150,
      "metadata": {
        "id": "QQQdFHWfkBLw"
      },
      "outputs": [],
      "source": [
        "rf_reg = ensemble.RandomForestRegressor(\n",
        "    n_estimators = 200,\n",
        "    max_depth = 12,\n",
        "    criterion = 'squared_error',\n",
        "    min_samples_split = 20,\n",
        "    random_state = 42,\n",
        "    n_jobs = -1\n",
        ")\n",
        "\n",
        "rf_reg.fit(X_train, y_train_log)\n",
        "\n",
        "y_train_pred = rf_reg.predict(X_train)\n",
        "y_valid_pred = rf_reg.predict(X_valid)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#RMSLE на тренировочной выборке\n",
        "round(np.sqrt(metrics.mean_squared_error(y_train_log, y_train_pred)), 2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6GkDpj5jv3PJ",
        "outputId": "2616dd44-cfbb-42e8-8b0c-2b2e7ee90a12"
      },
      "execution_count": 151,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.4"
            ]
          },
          "metadata": {},
          "execution_count": 151
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#RMSLE на валидационной выборке\n",
        "round(np.sqrt(metrics.mean_squared_error(y_valid_log, y_valid_pred)), 2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EuN_RbiUv-Im",
        "outputId": "3ddc90eb-b992-4286-ddf5-b15067026429"
      },
      "execution_count": 152,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.41"
            ]
          },
          "metadata": {},
          "execution_count": 152
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JZz_qWKDkBLw"
      },
      "source": [
        "### Задание 6.2.\n",
        "Постройте модель градиентного бустинга над деревьями решений (GradientBoostingRegressor) на обучающей выборке (факторы должны быть нормализованы, целевую переменную используйте в логарифмическом масштабе). В качестве гиперпараметров укажите следующие:\n",
        "* learning_rate=0.5,\n",
        "* n_estimators=100,\n",
        "* max_depth=6,\n",
        "* min_samples_split=30,\n",
        "* random_state=42\n",
        "\n",
        "Для полученной модели рассчитайте метрику RMSLE на тренировочной и валидационной выборках. Ответ округлите до сотых.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 153,
      "metadata": {
        "id": "KnnmuhoSkBLw"
      },
      "outputs": [],
      "source": [
        "# ваш код здесь\n",
        "gb_reg = ensemble.GradientBoostingRegressor(\n",
        "    learning_rate = 0.5,\n",
        "    n_estimators = 100,\n",
        "    max_depth = 6,\n",
        "    min_samples_split = 30,\n",
        "    random_state = 42\n",
        ")\n",
        "gb_reg.fit(X_train, y_train_log)\n",
        "\n",
        "y_train_pred = gb_reg.predict(X_train)\n",
        "y_valid_pred = gb_reg.predict(X_valid)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#RMSLE на тренировочной выборке\n",
        "round(np.sqrt(metrics.mean_squared_error(y_train_log, y_train_pred)), 2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TCJVhwPGwOZ7",
        "outputId": "afc1e3d6-868f-4616-8223-1cfea838f5ad"
      },
      "execution_count": 154,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.37"
            ]
          },
          "metadata": {},
          "execution_count": 154
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#RMSLE на валидационной выборке\n",
        "round(np.sqrt(metrics.mean_squared_error(y_valid_log, y_valid_pred)), 2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qSWxkdyHwSmA",
        "outputId": "5655f558-e162-45b7-d6d1-c050dde53d27"
      },
      "execution_count": 155,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.39"
            ]
          },
          "metadata": {},
          "execution_count": 155
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zC3NKXLhkBLw"
      },
      "source": [
        "### Задание 6.3.\n",
        "Какая из построенных вами моделей показала наилучший результат (наименьшее значение RMSLE на валидационной выборке)?\n",
        "* Линейная регрессия\n",
        "* Полиномиальная регрессия 2ой степени\n",
        "* Дерево решений\n",
        "* Случайный лес\n",
        "* Градиентный бустинг над деревьями решений - эта модель наилучшая (RMSLE 0.39)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eaBYTxnkkBLx"
      },
      "source": [
        "### Задание 6.4.\n",
        "Постройте столбчатую диаграмму коэффициентов значимости каждого из факторов.\n",
        "\n",
        "Укажите топ-3 наиболее значимых для предсказания целевого признака - длительности поездки в логарифмическом масштабе - факторов.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 156,
      "metadata": {
        "id": "KmYOePR8kBLx",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 612
        },
        "outputId": "40a037e2-15c4-45f9-929d-a5877ee38108"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1300x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# ваш код здесь\n",
        "fig, ax = plt.subplots(figsize = (13, 5))\n",
        "importances = gb_reg.feature_importances_\n",
        "\n",
        "sns.barplot(x = features, y = importances, ax = ax)\n",
        "ax.set_title('График значимости признаков')\n",
        "ax.set_ylabel('Важность')\n",
        "ax.xaxis.set_tick_params(rotation = 90)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Три наиболее значимых - pickup_hour, total_distance, total_travel_time"
      ],
      "metadata": {
        "id": "y4Eobq7L1FoR"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TQ4NzZaTkBLx"
      },
      "source": [
        "### Задание 6.5.\n",
        "Для лучшей из построенных моделей рассчитайте медианную абсолютную ошибку (MeAE - в sklearn функция median_absolute_error) предсказания длительности поездки такси на валидационной выборке:\n",
        "$$ MeAE = median(|y_i-\\hat{y_i}|)$$\n",
        "\n",
        "Значение метрики MeAE переведите в минуты и округлите до десятых.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 164,
      "metadata": {
        "id": "mOYteyJ-kBLx",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8ab7f671-8227-4f23-f084-9d370b4bc59b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.8"
            ]
          },
          "metadata": {},
          "execution_count": 164
        }
      ],
      "source": [
        "# ваш код здесь\n",
        "y_valid = np.exp(y_valid_log) - 1\n",
        "y_valid_pred_exp = np.exp(y_valid_pred) - 1\n",
        "meae_valid = metrics.median_absolute_error(y_valid, y_valid_pred_exp)\n",
        "round(meae_valid / 60, 1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NZzsSUFfkBLx"
      },
      "source": [
        "Финальный шаг - сделать submit -  предсказание для отложенного тестового набора данных."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J12UJrZOkBLx"
      },
      "source": [
        "Прочитаем тестовые данные и заранее выделим столбец с идентификаторами поездок из тестового набора данных. Он нам еще пригодится:\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 163,
      "metadata": {
        "id": "5KAzvZidkBLx"
      },
      "outputs": [],
      "source": [
        "test_data = pd.read_csv(\"gdrive/My Drive/Project5_test_data.csv\")\n",
        "osrm_data_test = pd.read_csv(\"gdrive/My Drive/Project5_osrm_data_test.csv\")\n",
        "test_id = test_data['id']"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sdrR42JrkBLy"
      },
      "source": [
        "Перед созданием прогноза для тестовой выборки необходимо произвести все манипуляции с данными, которые мы производили с тренировочной выборкой, а именно:\n",
        "* Перевести признак pickup_datetime в формат datetime;\n",
        "* Добавить новые признаки (временные, географические, погодные и другие факторы);\n",
        "* Произвести очистку данных от пропусков;\n",
        "* Произвести кодировку категориальных признаков:\n",
        "    * Закодировать бинарные признаки;\n",
        "    * Закодировать номинальные признаки с помощью обученного на тренировочной выборке OneHotEncoder’а;\n",
        "* Сформировать матрицу наблюдений, оставив в таблице только те признаки, которые были отобраны с помощью SelectKBest;\n",
        "* Нормализовать данные с помощью обученного на тренировочной выборке MinMaxScaler’а.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 165,
      "metadata": {
        "id": "nqWQbqhGkBLy",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "13e2976a-2521-4f1b-88e6-33b6fbe304a0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Shape of data: (625134, 25)\n"
          ]
        }
      ],
      "source": [
        "test_data['pickup_datetime']=pd.to_datetime(test_data['pickup_datetime'],format='%Y-%m-%d %H:%M:%S')\n",
        "test_data = add_datetime_features(test_data)\n",
        "test_data = add_holiday_features(test_data, holiday_data)\n",
        "test_data = add_osrm_features(test_data, osrm_data_test)\n",
        "test_data = add_geographical_features(test_data)\n",
        "test_data = add_cluster_features(test_data, kmeans)\n",
        "test_data = add_weather_features(test_data, weather_data)\n",
        "test_data = fill_null_weather_data(test_data)\n",
        "\n",
        "test_data['vendor_id'] = test_data['vendor_id'].apply(lambda x: 0 if x == 1 else 1)\n",
        "test_data['store_and_fwd_flag'] = test_data['store_and_fwd_flag'].apply(lambda x: 0 if x == 'N' else 1)\n",
        "test_data_onehot = one_hot_encoder.fit_transform(test_data[columns_to_change]).toarray()\n",
        "column_names = one_hot_encoder.get_feature_names_out(columns_to_change)\n",
        "test_data_onehot = pd.DataFrame(test_data_onehot, columns=column_names)\n",
        "\n",
        "test_data = pd.concat(\n",
        "    [test_data.reset_index(drop=True).drop(columns_to_change, axis=1), test_data_onehot],\n",
        "    axis=1\n",
        ")\n",
        "X_test = test_data[features]\n",
        "X_test_scaled = mm_scaler.transform(X_test)\n",
        "print('Shape of data: {}'.format(X_test.shape))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "24JJ64T4kBLy"
      },
      "source": [
        "Только после выполнения всех этих шагов можно сделать предсказание длительности поездки для тестовой выборки. Не забудьте перевести предсказания из логарифмического масштаба в истинный, используя формулу:\n",
        "$$y_i=exp(z_i)-1$$\n",
        "\n",
        "После того, как вы сформируете предсказание длительности поездок на тестовой выборке вам необходимо будет создать submission-файл в формате csv, отправить его на платформу Kaggle и посмотреть на результирующее значение метрики RMSLE на тестовой выборке.\n",
        "\n",
        "Код для создания submission-файла:\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_test_pred_log = gb_reg.predict(X_test_scaled)\n",
        "y_test_predict = np.exp(y_test_pred_log) - 1"
      ],
      "metadata": {
        "id": "JSliGBb38_ZC"
      },
      "execution_count": 167,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 168,
      "metadata": {
        "id": "JeMAzrHHkBLy"
      },
      "outputs": [],
      "source": [
        "# ваш код здесь\n",
        "submission = pd.DataFrame({'id': test_id, 'trip_duration': y_test_predict})\n",
        "submission.to_csv('gdrive/My Drive/submission.csv', index=False)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iU5BA_XDkBLy"
      },
      "source": [
        "### **В качестве бонуса**\n",
        "\n",
        "В завершение по ансамблевым мы предлагаем вам попробовать улучшить свое предсказание, воспользовавшись моделью экстремального градиентного бустинга (XGBoost) из библиотеки xgboost.\n",
        "\n",
        "**XGBoost** - современная модель машинного обучения, которая является продолжением идеи градиентного бустинга Фридмана. У нее есть несколько преимуществ по сравнению с классической моделью градиентного бустинга из библиотеки sklearn: повышенная производительность путем параллелизации процесса обучения, повышенное качество решения за счет усовершенствования алгоритма бустинга, меньшая склонность к переобучению и широкий функционал возможности управления параметрами модели.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_FARWLbakBLy"
      },
      "source": [
        "Для ее использования необходимо для начала установить пакет xgboost:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 169,
      "metadata": {
        "id": "h8KXUhyGkBLy",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bb807df3-5c18-4418-cd92-6b17dd5c5607"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting xgboost\n",
            "  Downloading xgboost-2.0.3-py3-none-manylinux2014_x86_64.whl (297.1 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m297.1/297.1 MB\u001b[0m \u001b[31m3.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting catboost\n",
            "  Downloading catboost-1.2.5-cp310-cp310-manylinux2014_x86_64.whl (98.2 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m98.2/98.2 MB\u001b[0m \u001b[31m9.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from xgboost) (1.25.2)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.10/dist-packages (from xgboost) (1.11.4)\n",
            "Collecting graphviz (from catboost)\n",
            "  Downloading graphviz-0.20.3-py3-none-any.whl (47 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m47.1/47.1 kB\u001b[0m \u001b[31m5.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: matplotlib in /usr/local/lib/python3.10/dist-packages (from catboost) (3.7.1)\n",
            "Requirement already satisfied: pandas>=0.24 in /usr/local/lib/python3.10/dist-packages (from catboost) (2.0.3)\n",
            "Requirement already satisfied: plotly in /usr/local/lib/python3.10/dist-packages (from catboost) (5.15.0)\n",
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            "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.10/dist-packages (from pandas>=0.24->catboost) (2.9.0.post0)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=0.24->catboost) (2024.1)\n",
            "Requirement already satisfied: tzdata>=2022.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=0.24->catboost) (2024.1)\n",
            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->catboost) (1.2.1)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib->catboost) (0.12.1)\n",
            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->catboost) (4.53.0)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->catboost) (1.4.5)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->catboost) (24.0)\n",
            "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib->catboost) (10.3.0)\n",
            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->catboost) (3.1.2)\n",
            "Requirement already satisfied: tenacity>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from plotly->catboost) (8.3.0)\n",
            "Installing collected packages: graphviz, xgboost, catboost\n",
            "Successfully installed catboost-1.2.5 graphviz-0.20.3 xgboost-2.0.3\n"
          ]
        }
      ],
      "source": [
        "!pip install xgboost catboost"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "urka0597kBLz"
      },
      "source": [
        "После чего модуль можно импортировать:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 170,
      "metadata": {
        "id": "kpLIalxfkBLz"
      },
      "outputs": [],
      "source": [
        "import xgboost as xgb"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CwJ80NU7kBLz"
      },
      "source": [
        "Перед обучением модели необходимо перевести наборы данных в тип данных xgboost.DMatrix:"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "features_list = features.tolist()"
      ],
      "metadata": {
        "id": "MTp01fDm-Hmn"
      },
      "execution_count": 180,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 181,
      "metadata": {
        "id": "ehrdmZJekBLz"
      },
      "outputs": [],
      "source": [
        "# Создание матриц наблюдений в формате DMatrix\n",
        "dtrain = xgb.DMatrix(X_train, label=y_train_log, feature_names=features_list)\n",
        "dvalid = xgb.DMatrix(X_valid, label=y_valid_log, feature_names=features_list)\n",
        "dtest = xgb.DMatrix(X_test_scaled, feature_names=features_list)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kW0LAucxkBLz"
      },
      "source": [
        "Обучение модели XGBoost происходит с помощью метода train, в который необходимо передать параметры модели, набор данных, количество базовых моделей в ансамбле, а также дополнительные параметры:\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 182,
      "metadata": {
        "id": "u0uDs1YakBLz",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3d29bb41-611d-470d-b313-238d1c0c08e8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0]\ttrain-rmse:0.73991\tvalid-rmse:0.74191\n",
            "[10]\ttrain-rmse:0.49091\tvalid-rmse:0.49350\n",
            "[20]\ttrain-rmse:0.44059\tvalid-rmse:0.44349\n",
            "[30]\ttrain-rmse:0.42632\tvalid-rmse:0.42939\n",
            "[40]\ttrain-rmse:0.41827\tvalid-rmse:0.42149\n",
            "[50]\ttrain-rmse:0.41368\tvalid-rmse:0.41718\n",
            "[60]\ttrain-rmse:0.40965\tvalid-rmse:0.41342\n",
            "[70]\ttrain-rmse:0.40665\tvalid-rmse:0.41071\n",
            "[80]\ttrain-rmse:0.40411\tvalid-rmse:0.40850\n",
            "[90]\ttrain-rmse:0.40228\tvalid-rmse:0.40690\n",
            "[100]\ttrain-rmse:0.40040\tvalid-rmse:0.40525\n",
            "[110]\ttrain-rmse:0.39923\tvalid-rmse:0.40438\n",
            "[120]\ttrain-rmse:0.39789\tvalid-rmse:0.40331\n",
            "[130]\ttrain-rmse:0.39646\tvalid-rmse:0.40220\n",
            "[140]\ttrain-rmse:0.39550\tvalid-rmse:0.40146\n",
            "[150]\ttrain-rmse:0.39448\tvalid-rmse:0.40072\n",
            "[160]\ttrain-rmse:0.39352\tvalid-rmse:0.39998\n",
            "[170]\ttrain-rmse:0.39261\tvalid-rmse:0.39927\n",
            "[180]\ttrain-rmse:0.39184\tvalid-rmse:0.39882\n",
            "[190]\ttrain-rmse:0.39098\tvalid-rmse:0.39827\n",
            "[200]\ttrain-rmse:0.39005\tvalid-rmse:0.39772\n",
            "[210]\ttrain-rmse:0.38933\tvalid-rmse:0.39729\n",
            "[220]\ttrain-rmse:0.38844\tvalid-rmse:0.39666\n",
            "[230]\ttrain-rmse:0.38789\tvalid-rmse:0.39631\n",
            "[240]\ttrain-rmse:0.38722\tvalid-rmse:0.39591\n",
            "[250]\ttrain-rmse:0.38652\tvalid-rmse:0.39547\n",
            "[260]\ttrain-rmse:0.38601\tvalid-rmse:0.39524\n",
            "[270]\ttrain-rmse:0.38543\tvalid-rmse:0.39495\n",
            "[280]\ttrain-rmse:0.38478\tvalid-rmse:0.39456\n",
            "[290]\ttrain-rmse:0.38429\tvalid-rmse:0.39444\n",
            "[299]\ttrain-rmse:0.38381\tvalid-rmse:0.39419\n"
          ]
        }
      ],
      "source": [
        "# Гиперпараметры модели\n",
        "xgb_pars = {'min_child_weight': 20, 'eta': 0.1, 'colsample_bytree': 0.9,\n",
        "            'max_depth': 6, 'subsample': 0.9, 'lambda': 1, 'nthread': -1,\n",
        "            'booster' : 'gbtree', 'eval_metric': 'rmse', 'objective': 'reg:squarederror'\n",
        "           }\n",
        "# Тренировочная и валидационная выборка\n",
        "watchlist = [(dtrain, 'train'), (dvalid, 'valid')]\n",
        "# Обучаем модель XGBoost\n",
        "model = xgb.train(\n",
        "    params=xgb_pars, #гиперпараметры модели\n",
        "    dtrain=dtrain, #обучающая выборка\n",
        "    num_boost_round=300, #количество моделей в ансамбле\n",
        "    evals=watchlist, #выборки, на которых считается матрица\n",
        "    early_stopping_rounds=20, #раняя остановка\n",
        "    maximize=False, #смена поиска максимума на минимум\n",
        "    verbose_eval=10 #шаг, через который происходит отображение метрик\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nDiMljt5kBLz"
      },
      "source": [
        "Предсказать целевой признак на новых данных можно с помощью метода predict():"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 183,
      "metadata": {
        "id": "XYBTeO7UkBLz",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "74ea82a6-bb30-47a7-b3ba-40882fb57f4a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Modeling RMSLE 0.39419\n"
          ]
        }
      ],
      "source": [
        "#Делаем предсказание на тестовом наборе данных\n",
        "y_test_predict = np.exp(model.predict(dtest)) - 1\n",
        "print('Modeling RMSLE %.5f' % model.best_score)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jgBn4xmikBLz"
      },
      "source": [
        "Также как и все модели, основанные на использовании деревьев решений в качестве базовых моделей, XGBoost имеет возможность определения коэффициентов важности факторов. Более того, в библиотеку встроена возможность визуализации важность факторов в виде столбчатой диаграммы. За эту возможность отвечает функция plot_importance():\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 184,
      "metadata": {
        "id": "FEvlaqz4kBLz",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "b1696932-bd98-4c03-ab72-a7c7e6ed36fa"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: title={'center': 'Feature importance'}, xlabel='F score', ylabel='Features'>"
            ]
          },
          "metadata": {},
          "execution_count": 184
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x1500 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ],
      "source": [
        "fig, ax = plt.subplots(figsize = (15,15))\n",
        "xgb.plot_importance(model, ax = ax, height=0.5)"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "V28",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.11.7"
    },
    "accelerator": "TPU"
  },
  "nbformat": 4,
  "nbformat_minor": 0
}